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2002

Intrinsic biodegradation potential of crude oil in salt marshes Intrinsic biodegradation potential of crude oil in salt marshes

Julius Enock Louisiana State University and Agricultural and Mechanical College

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INTRINSIC BIODEGRADATION POTENTIAL OF CRUDE OIL IN SALT MARSHES

A Thesis Submitted to the Graduate Faculty of the

Louisiana State University and Agricultural and Mechanical College

in partial fulfillment of the requirements of the degree of

Master of Science in Civil Engineering

in

The Department of Civil and Environmental Engineering

by Julius Enock

B. Sc. (Eng.), University of Dar-es-salaam, 1998 August 2002

To my dear wife, Demetria, for being the light of my life, and

to my baby son, Joel, for giving me new eyes to see the world

ii

ACKNOWLEDGEMENTS

The author wish to extend profound appreciation to his Major Professor, Dr.

John H. Pardue, for the relentless and intuitive guidance, which helped in sharpening the

focus of the research effort. It has been a stimulating and rewarding experience to work

with him.

The author is indebted to the members of his advisory committee, Dr. William

M. Moe and Dr. Clinton. S. Willson, for accepting to serve in the committee and

rendering generously their time and expertise.

The author expresses his gratitude to present and former Wetland Research

Group members for the helping hand and sharing moments and experiences. Thanks are

due to Gabriel Kassenga, Jason House, Ms Eun-Ju Lee, Dr. Cesar Gomez, Ms Lizhu

Lin, Dr. Sangjin Lee, Stephen Mbuligwe and Jeff Maynor.

The author is thankful to Ms Sarah C. Jones, for facilitating the analytical work

involving the Spectrophotometer. Also, the author appreciates the service provided by

the Graduate School Editor, Ms Susanna Dixon.

The author gratefully acknowledges jointly the USAID (United States Agency

for International Development), the Government of the United Republic of Tanzania

and the Africa-America Institute (AAI), for the award of the ATLAS (African Training

for Leadership and Skills) scholarship. This has provided an opportunity to benefit from

the cutting-edge environmental research experience and know-how. Special thanks

should go to Ms R. Caldwell, AAI (New York), for the timely service and care.

Accordingly, the author would like to thank his employer, the Vice President’s Office

iii

(Division of Environment), Tanzania, for facilitating the necessary arrangements prior

to the studies.

The author is thankful to his former undergraduate mentors in particular, Dr.

Osmund K. Kaunde and Prof. Jamidu H. Y. Katima from the Chemical and Process

Engineering Department, University of Dar-es-salaam, Tanzania, who have helped and

have been case in point that have inspired his career.

The author extends special thanks to his wife, Demetria, for her unique blend of

love and understanding, inspiration, encouragement and support. And to his baby son,

Joel, whose presence has been a blessing with an unending joy.

Lastly, but not least, the author is grateful to his parents, Enock and Fausta

Moshi, who have loved and supported him through everything.

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TABLE OF CONTENTS

ACKNOWLEDGEMENTS …………………………………………………………..iii

LIST OF TABLES ……………………………………………………………………vii

LIST OF FIGURES ………………………………………………………………….viii

ABSTRACT ….……………...…………………………………………………………ix

CHAPTER 1. INTRODUCTION AND OUTLINE .....……………………………….1 1.1 Background ......…..……………………………………………………………..1 1.2 Research Objectives…..…………………………………...……………………4 1.3 Environmental Relevance of the Study .…………………………..……………4 1.4 Organization of the Thesis . ………………………….………...……………….5

CHAPTER 2. INTRINSIC BIODEGRADATION POTENTIAL OF CRUDE OIL IN MARINE SEDIMENTS: A REVIEW .….….…………...…....6

2.1 Introduction . ….......………………………………………..……………………6 2.2 Fractional Composition of Crude Oil .………………….……..………………...7 2.3 Effect of Crude Oil on Microbial Communities …………...……..……………..8 2.4 The Role of Soluble Organic Carbon ...………………………………..…….….9 2.5 Preferential Biodegradation of Crude Oil Fractions ………………….……..…10 2.6 Effect of Prior Exposure on Biodegradation Potential . ..……………………....10 2.7 Linking Flooding Effect to Microbial Activity . ..………………………..…….12 2.8 Summary and Implications. ..……………………………………....……..……13

CHAPTER 3. EFFECT OF FLOODING ON BIODEGRADATION POTENTIAL OF CRUDE OIL IN A SALT MARSH .......…....…...14

3.1 Introduction ........…………………..…………………………………….…….14 3.2 Materials and Methods ……......……………………………………………….16 3.3 Results ........……………………..………………………………….………….25 3.4 Discussion …...……………………..………………………………………….28 3.5 Conclusions and Implications.…...……..……………………………….……..37

CHAPTER 4. OIL SPILL RECURRENCE IN A SALT MARSH UNDER NATURAL RECOVERY ....................………………………...….…39

4.1 Introduction . ...…………………..……………………………………….…….39 4.2 Materials and Methods ...……………..…………..……………………………41 4.3 Results ...…………………………………..……………………….…………..48 4.4 Discussion...…………………………………..………………………………..51 4.5 Conclusions and Implications...…………………..…………………….……...59

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CHAPTER 5. SUMMARY AND OUTLOOK ..………………….…………………61 5.1 Experimental Findings and Implications .……..……………………………….61 5.2 Future Research .......………………………………..………………………….63

LITERATURE CITED …… ...…………..………………………………..…………..64

APPENDIX A. CALIBRATION CURVES .. ……....……………………………....…….……69 B. RESIDUAL HYDROCARBON DEGRADATION DATA ....………………70 C. MICROBIAL ACTIVITY (FDA HYDROLYSIS) DATA . .………………...74 D. SOLUBLE ORGANIC CARBON (SOC) DATA .. …….…….………………76 E. SAMPLE STEPWISE REGRESSION OUTPUT...…………………………78 F. PEARSON CORRELATION RESULTS ....……………………….………...80

VITA …....…………………………………………………….…………………...........82

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LIST OF TABLES

2.1 Typical chemical composition and selected physical properties of some crude oil samples…………………………………………………………………8

3.1 Summary results of first-order rate constants for alkanes and PAHs in the

flooding study ……..………………………...…………………………...……..30 4.1 Summary results of first-order rate constants for alkanes and PAHs in

the oil spill recurrence study .…………………………………………………...53

B1 Summary data for the degradation of alkanes for the flooding study.………….72 B2 Summary data for the degradation of PAHs in the flooding study …………….72 B3 Summary data for the degradation of alkanes in the oil spill recurrence study ...73 B4 Summary data for the degradation of PAHs in the oil spill recurrence study .…73 C1 Summary data of microbial activity (FDA hydrolysis) for flooding study .……74 C2 Summary data of microbial activity (FDA hydrolysis) for the oil spill

recurrence study …………………………...……………………………………75 D1 Summary data of SOC analysis for the flooding study .…………………….......76 D2 Summary data of SOC analysis for the oil spill recurrence study .....………......77 F1 The P-values from Pearson correlation for alkane degradation under the IF

regime …………………………………………………………………………..80 F2 The P-values from Pearson correlation for PAH degradation under the IF

regime…………………………………………………………………………..81 F3 The P-values from Pearson correlation for alkane degradation under the four

successive oiling treatment …………………………………………………......81 F4 The P-values from Pearson correlation for PAH degradation under the four

successive oiling treatment ……………………………………………………..81

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LIST OF FIGURES

3.1 Map of the study site located in the Leeville Oil Field ………...............................18 3.2 The effect of flooding regime on selected residual petroleum hydrocarbons …….29 3.3 The effect of flooding on microbial activity in terms of the amount of FDA

hydrolyzed ……………………………………………………………….………..31 3.4 The effect of flooding on soluble organic carbon (SOC) content ……………...…32 4.1 The effect of oil spill recurrence on the residual petroleum hydrocarbons …........52 4.2 The effect of oil spill recurrence on microbial activity in terms of the amount of

FDA hydrolyzed…………….……………………………………...……………..54 4.3 The effect of oil spill recurrence on soluble organic carbon (SOC) content ……..55 A1 The calibration curve for FDA analysis using spectrophotometer ...……………..69 A2 The calibration curve for SOC analysis using TOC analyzer ..…...….…………...69 B1 The effect of flooding on degradation of phenanthrene and pyrene …………...…70 B2 The effect of oil spill recurrence on degradation of phenanthrene and pyrene…...71

viii

ABSTRACT

Understanding the influence of different perturbations on the fate of spilled oil in

marine ecosystems is useful in assessing the environmental impact and remedial

investigation. The effect of flooding and spill recurrence on the fate of an experimental

crude oil spill (2 L/m2) was investigated using salt marsh intact cores, incubated for

about 3 months, by monitoring residual petroleum hydrocarbons, heterotrophic

microbial activity (fluorescein diacetate assay) and soluble organic carbon (SOC).

For the flooding study, biodegradation rate of crude oil (with half-lives varying

between 16.50 and 49.51 days and turnover times between 23.81 and 71.43 days) and

microbial activity increased significantly (P>0.05) in the order from continuously-

flooded (CF), intermittently-flooded (IF) to non-flooded (NF) regime. The SOC

increased significantly (P>0.05) in the opposite order. The results signify the influence

of flooding on microbial activity and indirectly affecting biodegradation of crude oil and

decomposition and accumulation of organic matter in salt marshes.

For the oil spill recurrence study (single, two, three and four successive oilings;

each totaling to 2 L/m2), biodegradation rate of crude oil (with half-lives varying

between 11.95 and 69.31 days and turnover times between 17.24 and 100.00 days),

microbial activity and SOC increased significantly (P>0.05) with each subsequent

oiling. The results suggest that, microbial degradation might not be significant in a

pristine tidal marsh particularly immediate to an oil spill event as opposed to a

previously contaminated one.

The lack of significant linear relationships (P>0.05) among the parameters

measured in both experiments, as indicated by both (forward) stepwise regression and

Pearson correlation, reflects the challenge in understanding the complex interaction of

environmental factors and microbial ecology in predicting the fate of spilled crude oil in

the salt marshes at least under the experimental conditions.

ix

CHAPTER 1

INTRODUCTION AND OUTLINE

1.1 Background

The coastal marshes and estuaries located in the southern United States

adjacent to the Gulf of Mexico, account for over 40% of the coastal wetlands of the

United States (Mitsch and Gosselink, 1993). These wetlands are remarkably

productive ecosystems that provide habitat, breeding, and nursery grounds for fish

and wildlife, oil and gas production, protection from shoreline erosion, and serves as

a buffer from hurricanes and other storms (Fleury, 2000; Rozas et al, 2000).

Among the coastal wetlands are the salt marshes, characterized by saline

conditions and emergent vegetation such as Spartina alterniflora (smooth cord grass)

in areas alternately flooded and drained by tides (Penfound and Hathaway, 1938;

Fleury, 2000). Notably, there is eminent threat from oil spills, due to oil shipping

tankers after accidents, oil exploration and development activities, rupture or leakage

from oil pipelines laid through the ocean, and even natural seeps. Oil is swept into

salt marshes by tidal currents and wind and is trapped by marsh grass and the

organic-rich sediments.

It is estimated that world annual oil spills into the ocean amounts to about

1.7-8.8 million metric tons, approximately equivalent to about 0.1 to 0.2% of the

world annual petroleum production (National Academy of Sciences, 1985;

Harayama et al, 1999). Worse still, experience from the Exxon Valdez oil spill

indicates that physical recovery of the spilled oil can hardly manage to reclaim 14%

1

of the spilled oil (Miller, 1999). Despite many of the crude oil components being

biodegradable through numerous microbial processes in the environment, some

recalcitrant ones may persist for longer periods in the soil from several years to

decades. Crude oil components are of environmental concern due to their toxic,

mutagenic, and carcinogenic properties (Nelson-Smith, 1973; Freedman, 1995;

Rozas et al, 2000). Consequently, crude oil is deleterious to a wide spectrum of

marine plants, animals and microbial communities, through oxygen stress (from

organic enrichment) and direct toxic effects (mortality) (Carman et al, 2000).

Generally speaking, oil spills into salt marsh ecosystems imparts potential damage to

their physical and ecological integrity even in minimal spill levels let alone

catastrophic accidents like the Exxon Valdez in 1989 (36,000 tones of crude oil

covered approximately 500 kilometers of shoreline) (Miller, 1999). With increasing

oil and gas operations along the Louisiana coastal zone (Jackson, 1996) and in view

of the ecological and economic benefits of the surrounding marshes and estuaries,

then understanding the fate of occasional oil spills into these ecosystems is of

significant interest, for their appropriate management in case of an oil spill.

The ultimate fate of oil spills in the marine environment is dictated by a set of

biotic and abiotic processes including spreading and drifting, emulsification,

evaporation, dissolution, photochemical oxidation and microbial degradation

(Nelson-Smith, 1973; Lee, 1980). Previous studies have established that microbial

degradation is an important process in determining the fate of spilled oil trapped in

coastal marsh sediments, and is estimated to contribute in the removal of as much as

40-80% of the spilled oil (Christian and Wiebe, 1978; Lee, 1980). In addition, salt

2

marshes are sensitive ecosystems (even foot traffic can cause substantial damage),

implying that less intrusive, biodegradation-based remedial alternatives are the

suitable option since they present minimum harm to these ecosystems (Jackson,

1996). It follows that, the present work was undertaken to obtain a better

understanding of the influence of selected factors on intrinsic biodegradation of

crude oil in salt marshes.

Noteworthy, biodegradation of crude oil in the environment occurs at varying

rates, depending on numerous physical and biogeochemical perturbations imposed

onto these ecosystems. These perturbations include such factors as temperature

(Atlas, 1981), sedimentation, wind, precipitation and tidal flooding (Wright et al,

1997). The logic follows that, one of the pressing research needs for biodegradation-

based oil spill remediation strategies is determining and evaluating the biotic and

abiotic factors that influence the fate of the spilled oil and devising ways to

accelerate the biodegradation rate.

As it will be revealed in the following three chapters, limited information is

available with regard to the effect of tidal flooding and oil spill recurrence on the

biodegradation of crude oil in salt marshes. Fundamental to this, oil spills in salt

marshes tend to alter the nature and extent of microbial populations and diversity and

soil characteristics (Leahy and Colwell, 1990; Nyman, 1999) with potentially

important effects on oil-degrading microbial processes. Further, heterotrophic

microbes are known to play a major role in organic matter decomposition and

assimilation and are of considerable importance in nutrient mineralization

(Freedman, 1995; Hunter, 2000). In that perspective, this study sought to examine

3

the influence of flooding and oil spill recurrence on the biodegradation rate of crude

oil, soil heterotrophic microbial activity and water-soluble organic carbon.

1.2 Research Objectives

The intent of this study in relation to the fate of crude oil in salt marshes has

been indicated. The specific objectives were to:

(i) Evaluate the influence of batch-flooding – non-flooded, continuously

flooded and intermittently flooded regimes - on the biodegradation rate of

crude oil in salt marsh intact cores.

(ii) Compare the biodegradation rate of crude oil between single and multiple

successive oiling of the same total volume (2 L/m2).

(iii) Evaluate the effect of flooding and oil spill recurrence on selected soil

biogeochemical parameters namely, heterotrophic microbial activity and

soluble organic carbon, in artificially oil-contaminated salt marsh intact

cores

(iv) To relate intrinsic biodegradation potential of crude oil in salt marshes to

microbial activity and soluble organic carbon under the influence of

flooding and oil spill recurrence.

1.3 Environmental Relevance of the Study

Crude oil spills in the marine environment is one of the major pollution

problems in the US and worldwide. Notably, salt marshes are inaccessible for

physical remedial schemes and they are ecologically sensitive areas particularly

4

when impacted by oil spills and can trap large quantities of oil and therefore, they

may provide a challenge in the clean up.

Microbial biodegradation is one of the principal processes for removal of

non-volatile crude oil components from oil-contaminated marine sediments. Clearly,

environmental restoration from oil spills focuses on the need for environmental

benign strategies. Therefore, gaining a better understanding of the factors influencing

the biodegradation of spilled oil and soil physico-chemical and biological functions

is an important step in the assessment of the environmental impact of oil spills and in

developing and/or improving existing biodegradation-based remediation strategies.

1.4 Organization of the Thesis

Chapter 2 reviews selected aspects on the fate of spilled oil in marine

sediments. Chapter 3 presents results of the flooding effect on biodegradation of

crude oil, heterotrophic microbial activity and soluble organic carbon (SOC).

Succeeding Chapter 4, details the results of a study on the influence of oil spill

recurrence on biodegradation of crude oil in salt marshes in relation to residual

petroleum hydrocarbons, microbial activity and SOC. Following this, Chapter 5

summarises the results from this work and highlights some areas for future research.

5

CHAPTER 2

INTRINSIC BIODEGRATION POTENTIAL OF CRUDE OIL IN MARINE SEDIMENTS: A REVIEW

2.1 Introduction

While the amount of annual oil spills in the marine environment is significant, the

coastal marshes and estuaries are constantly at risk from oil pollution from accidents,

leakage or rupture of oil pipelines, oil and gas exploration, and even natural seeps. The

adverse environmental impact of oil contamination in these marine ecosystems can not be

overemphasized. As a result, increasing attention is being focused on understanding the

fate of oil spills in the environment and the weathering mechanisms (Lee, 1980; Atlas,

1981; Berry et al, 1987; Leahy and Colwell, 1990; Harayama et al, 1999). The unique

features of these coastal marshes such as organic-rich sediments and anoxic conditions

favour the accumulation and penetration of oil in the soil. Oil penetration through soil

reduces aeration and upsets the carbon/inorganic nutrient balance for the indigenous

microbial communities (Riser-Roberts, 1998), which indirectly affects the fate of the oil

trapped in the sediments. Although the microbial degradation of petroleum hydrocarbons

in the environment is well established (Edwards and Grbic-Galic, 1994; Long et al, 1995;

Lovley et al, 1995; Coates et al, 1996; Vroblesky et al, 1997; Caldwell et al, 1998; Shin,

1998; Phelps and Young, 1999; Nyman, 1999; Pardue et al, 2001; El-Tarabily, 2002),

however, the importance of indigenous microbial activity and processes has only recently

attracted considerable interest due to the increased incidence of major oil accidents.

Since the degradation of trapped oil in marine sediments is mainly mediated by

microbes, biodegradation rates are therefore, dependent on the microbial activity and

6

environmental factors influencing microbial processes (Atlas, 1981; Leahy and Colwell,

1990; Freedman, 1995; Sugai et al, 1997). As a result, gaining fundamental knowledge of

the interaction of environmental factors and microbial activity may be of interest in an

attempt to improve existing oil-spill remediation processes and developing novel ones.

The primary effort of this chapter is to provide a brief overview on the impact of

oil spills on microbial activity and functions and the influence of selected environmental

and physical factors on the biodegradation of petroleum hydrocarbons in marine

sediments.

2.2 Fractional Composition of Crude Oil

Crude oil is a complex mixture of hydrocarbons, varying widely in both physical

and chemical properties depending on the source (Atlas, 1981; Leahy and Colwell, 1990).

Crude oil may be characterized in terms of four primary fractions, namely saturates,

aromatics, resins and asphaltenes with an average density of 850 kg/m3 (Connell and

Miller, 1981; Harayama et al, 1999). Saturates include straight or branched chain n-

alkanes, and the cycloalkanes with one or more saturated rings, while aromatics include

compounds with one or more fused aromatic rings, each of which may be attached

saturated side chains (alkyl-substituents). In contrast to the saturated and aromatic

fractions, both the resins and asphaltenes consist of non-hydrocarbon polar compounds,

with trace amounts of nitrogen, sulfur and/or oxygen in addition to carbon and hydrogen,

and often forming complexes with heavy metals. For the sake of clarity, asphaltenes

consist of high-molecular-weight compounds, which are not soluble in a solvent such as

n-heptane, while resins are n-heptane-soluble molecules, principally containing

7

heterocyclic compounds, acids and sulfoxides (Harayama et al, 1999). Typical chemical

composition of different crude oils is presented in Table 2.1.

Table 2.1: Typical chemical composition and selected physical properties of some crude oil samples (Connell and Miller, 1981)

% Composition (w/w)

Component “Sweet” Louisiana

crude oil

Kuwait

Saturates (n-alkanes and cycloalkanes) 56.30 34.00

Aromatics 35.10 44.60

Resins (polar and insoluble substances) 8.60 21.40

Sulfur 0.25 2.44

Nitrogen 0.069 0.14

Nickel (ppm) 2.2 7.70

Vanadium (ppm) 1.9 28.00

API gravity 34.50 (15.5oC) 31.40 (15.5oC)

Specific gravity 0.8524 0.8686

2.3 Effect of Crude Oil on Microbial Communities

While it is important to understand and identify environmental and other

constraints that affect biodegradation of trapped oil in marine sediments and soils, it is

necessary to assess and quantify microbial activity and functions of the impacted soils.

8

Crude oil is known to reduce microbial diversity in marine sediments and

enrichment of oil-degrading microbes has been widely reported (Pfaender and Buckley,

1984; Nyman, 1999). Several studies have found little or no effect of oil on abundance of

soil microbial community (DeLaune et al, 1979; Nyman, 1999) while others have noted

adverse effects on oil microorganism populations (Jackson, 1996; El-Tarabily, 2002).

Similarly, Li et al (1990) found that high levels (33.3 g C m-2 day-1) of a mixture of 10

petroleum hydrocarbons inhibited microbial respiration and nutrient re-mineralization in

salt marsh soils, but low levels (3.33 g C m-2 day-1 ) of the hydrocarbon mixture

stimulated microbial activity.

It can be assumed that, the exposure of microorganisms to petroleum

hydrocarbons may be stimulatory, inhibitory or neutral and the degree and duration of the

impact is a function of the concentration and chemical composition and environmental

factors (Pfaender and Buckley, 1984)

2.4 The Role of Soluble Organic Carbon (SOC)

Heterotrophic microorganisms are important in soil nutrient mineralization and

decomposition of organic matter (Nyman, 1999). Noteworthy, the microbial population

size and activity in soil is influenced by the quantity and quality of organic matter (i.e.

readiness for utilization by microorganisms). Soluble organic carbon (SOC) is one of the

labile fractions of dissolved organic matter considered to be a critical carbon and energy

source for heterotrophic microbes (Hunter, 2000). Although SOC consists of a mixture of

simple substances such as sugars, fatty acid and alkanes and relatively small fraction of

complex polymeric molecules, it is well known that not all SOC is labile (Marschner and

Bredow, 2002).

9

Microbial populations in soils rely on organic matter as a source of carbon, other

nutrients and growth factors. Therefore, the availability of SOC may be a significant

factor in determining the fate of crude oil in marine sediments and it may well compete

with petroleum hydrocarbons as a substrate for the oil-degrading microbes. For instance,

Hebert et al (1993) observed pyrene partitioning to SOC to be significant depending on

its concentration in soil solution and its lability. This implies that, the SOC may decrease

the aqueous concentration of a sorbed hydrocarbon while providing additional source of

carbon and energy to microbes and this may enhance or inhibit degradation rate of the

hydrocarbon depending on the importance of one process over the other. However, there

is no available information on the competitive metabolism of SOC and petroleum

hydrocarbons and this may be worth exploring.

2.5 Preferential Biodegradation of Crude Oil Fractions

Many genera of microbes are able to completely oxidize alkanes and to a lesser

extent, aromatic hydrocarbons (Jackson, 1996). Based on previous studies and reviews on

biodegradation of petroleum hydrocarbons in the marine environment, several

generalizations can be made (Atlas, 1988; Jackson, 1996; Harayama et al, 1999):

• Straight chain aliphatic hydrocarbons are easier to be degraded than branched

chain aliphatic hydrocarbons.

• Aliphatic hydrocarbons are degraded more easily than aromatic hydrocarbons.

• Saturated hydrocarbons are more easily degraded than unsaturated hydrocarbons.

10

• Long chain aliphatic hydrocarbons are more easily degraded than short chain

(<C10) hydrocarbons, with few exceptions, since the latter are essentially toxic to

microorganisms.

• Asphaltenes (and resins) are the most recalcitrant fractions in the crude oil.

2.6 Effect of Prior Exposure on Biodegradation Potential

It known that prior exposure of petroleum hydrocarbons may result into

accelerated biodegradation of the subsequent additions. A brief summary on the effect of

prior exposure and microbial adaptation to petroleum hydrocarbons on biodegradation

potential is included in the review paper by Leahy and Colwell (1990). Microbial

communities adapt to contaminants such as crude oil by enrichment of those

microorganisms that are either resistant to the toxic effects of the contaminant, or capable

of utilizing the contaminant as a nutrient source (Atlas, 1981). Also, microbial adaptation

has been associated with changes in specific metabolic enzyme and genetic alterations

resulting in enhanced metabolic capabilities (Leahy and Colwell, 1990).

Since the marine ecosystems are constantly at risk from oil spills, repetitive spills

on the same location, due to tidal effect or simply repeated spill, may have a range of

effects on the biodegradation potential of crude oil and ultimately on the functional

recovery of these ecosystems. However, limited information is available as regards to the

influence of spill recurrence especially of complex hydrocarbon mixture such as crude oil

on biodegradation potential in marine sediments and soils.

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2.7 Linking Flooding Effect to Microbial Activity

In an ecological context, tidal flooding may be considered as a major physical

disturbance that can result in large changes in soil aeration status and sediment

biogeochemical characteristics such as redox potential, pH (Gambrell and Patrick Jr.,

1979; Hambrick, 1979) and organic matter decomposition (Christian and Wiebe, 1978;

Nyman and DeLaune, 1991). As a result, Sugai et al (1997) observed that sediment

chemistry data alone could not predict the persistence of petroleum hydrocarbons

following the Exxon Valdez oil spill and emphasized the need for studies of the abiotic

and biotic factors influencing biodegradation in the coastal marsh ecosystems.

Flooding is known to be associated with anaerobic conditions (Gambrell and

Patrick Jr., 1979; Nyman and DeLaune, 19991). The anaerobic biodegradation of

petroleum hydrocarbons in anoxic marine sediments has been reported to occur with

ferric iron (Beller et al, 1992), nitrate (Berry et al, 1987; Rockne et al, 2000), sulfate

(Berry et al, 1987; Beller et al, 1992; Lovley et al, 1995; Coates et al, 1996) and carbon

dioxide (Berry et al, 1987; Edwards and Grbic-Galic, 1994) acting as alternative electron

acceptors to oxygen.

Hambrick (1979) observed that by varying Eh from +130 mV (aerobic) to -220

mV (anaerobic), the degradation of [14 C] naphthalene decreased from 22.6% to only

about 0.62% for a period over 35 days. Similarly, Bauer and Capone (1985) observed that

anoxic sediments were more sensitive than aerobic sediments to anthracene and

naphthalene additions based on d-[U-14C]glucose metabolic activity and [methyl-

3H]thymidine incorporation activity. Both of these studies reflect possible decrease in

12

microbial population and/or activity under anaerobic conditions, which typically prevails

in coastal marine sediments primarily due to flooding.

2.8 Summary and Implications

The environmental impact of oil contamination in coastal marshes and estuaries is

potentially serious. The microbial degradation of petroleum hydrocarbons in marine

sediments is a rapidly growing research area with focus to develop a better understanding

of the fate of spilled oil in marine environment and devising remedial measures that fully

utilize the indigenous microbial assimilative capacity.

A great deal of information available recognizes the significance of microbial

degradation on the fate of trapped oil in marine sediments and acknowledges the

influence of a variety of biotic and abiotic factors. However, the linkage of tidal flooding

to overall microbial activity which in turn may influence the intrinsic biodegradation of

spilled oil in coastal marsh sediments is not well established. On the other hand, there is

an emerging question regarding the impact of oil spill recurrence on biodegradation

potential of complex mixtures of petroleum hydrocarbons such as crude oil in marine

sediments.

13

CHAPTER 3

EFFECT OF FLOODING ON BIODEGRADATION POTENTIAL OF CRUDE OIL IN A SALT MARSH

3.1 Introduction

The approximately 12.5 million hectares of salt marshes are a major and

important component of the coastal wetlands in the southern United States (Mitsch and

Gosselink, 1993). However, these salt marshes are vulnerable to oil spills from a variety

of sources including marine vessels after accidents, oil pipelines, oil and gas exploration

and natural seeps. Oil is swept into salt marshes by tidal currents and wind and is trapped

by marsh grass and organic-rich sediment. By and large, oil spills into coastal marshes

and estuaries can present potential damage and disruption of their physical and ecological

functions even in minimal spill levels. Salt marshes are known to have a significant

inherent capacity to degrade crude oil components (Jackson and Pardue, 1999). However,

our ability to utilize the indigenous microbial diversity and genetic versatility for

successful application of remedial measures, still relies on our understanding of the

interaction between environmental and ecological features within the marine ecosystems

and their influence on the fate of spilled oil.

As one of the oil-spill remediation strategies, nutrient (fertilizer) amendment is

being advocated and has been demonstrated to enhance oil biodegradation in

experimental and actual oil-contaminated marine ecosystems. For instance, Jackson and

Pardue (1999) reported the application of ammonium sulfate in Louisiana salt marsh

microcosms, doubling the biodegradation rate of crude oil. Similar laboratory results

have been widely reported in salt marshes (Wright et al, 1997; Shin, 1998), as well as in

14

other marine ecosystems such as mangroves (Scherrer and Mille, 1990). Also, a number

of successful full-scale bioremediation projects have been reported (Bragg et al, 1994;

Rozas et al, 2000) particularly the well known case of Exxon Valdez. However, in a

recent field trial in a salt marsh (Shin et al, 1999) and in some of the mangrove soils

(Scherrer and Mille, 1990), the addition of fertilizers did not show statistically significant

effect on the biodegradation rate of crude oil components. Among many factors, it was

hypothesized that the discrepancy in findings between field and laboratory studies may be

attributed to the influence of tidal flooding under field conditions. Tidal flooding may

directly or indirectly influence oxygen availability (De and Bose, 1938; Gambrell and

Patrick, 1979; Wright et al, 1997), and as hypothesized by Shin et al (1999), limiting

aerobic degradation of the trapped oil in marsh sediments and/or encouraging anaerobic

degradation pathway(s) that are not nutrient limited.

Typically, tidal flooding is a dominant physical feature of the salt marshes.

Flooding essentially restricts gaseous exchange between soil and air, increases pH, and

reduces redox potential (Hambrick, 1979; Nyman and DeLaune, 1991; Taylor III, 1995).

As a result, flooding influences the dynamics of nutrient exchange (Taylor III, 1995),

sedimentation (Adam, 1990), soil biogeochemical processes (DeLaune et al, 1979) and

vegetation development (Penfound and Hathaway, 1938; Christian and Wiebe, 1978).

This reflects that, tidal flooding is of ecological significance in salt marshes, and

therefore may have an important role on the physical and functional recovery of the salt

marshes following oil spills.

Limited studies have been undertaken to assess the influence of tidal flooding on

the biodegradation of crude oil and associated physico-chemical and biological function

15

of oil-contaminated marine ecosystems. A handful of nutrient-amendment laboratory-

based studies have demonstrated statistically significant effect of flooding on the

biodegradation of crude oil but not on the sulfate reduction rate (indirectly linked to crude

oil biodegradation) (Shin, 1998) neither on the number of heterotrophic bacteria (Wright

et al, 1997). Respectively, these results reflect that, other factors besides sulfate reduction

process may have contributed to the decrease in biodegradation rate observed and oil

pollution may be associated with possible increase in microbial metabolic activity per cell

rather than substantial increase in number of bacteria. Therefore, this merit further

scrutiny to explore other possible factors that may have contributed in limiting the

biodegradation rate of crude oil under flooding conditions.

Evidently, reliable prediction of the fate of spilled oil in coastal marsh sediments

requires knowledge on the influence of the various perturbations on both soil

geochemical and biological factors. Monitoring and exploring the interplay between these

processes is useful in assessing the environmental impact and recovery of oil-

contaminated marine ecosystems. In the present work, greenhouse experiments were

conducted using salt marsh intact cores growing Spartina alterniflora to investigate the

effect of batch-flooding on the biodegradation rate of crude oil, soil heterotrophic

microbial activity and soluble organic carbon (SOC).

3.2 Materials and Methods

3.2.1 Site Description

The study site is located near Port Fourchon at the southwestern end of the

Barataria Basin in Louisiana, as shown in Figure 3.1. While catering to several other

business sectors, the primary purpose of the port is to support offshore oil-and-gas

16

activities throughout the central Gulf of Mexico. The site is situated in the Leeville oil

field in the Lafourche Parish at approximately 29o14’ 52” N latitude and 90o 12’ 27’W

longitude. The climate is sub-tropical, with annual temperature averaging 15oC with a

mean annual low of 10 oC and a mean annual high of 30 oC. Average yearly precipitation

is about 157 cm/year.

The marsh site is flooded with diurnal tides of approximately 0.07-0.67 m in

magnitude, which are predominantly influenced by seasonal winds. The marsh site is

dominated by uniform stands of Spartina alterniflora plants.

3.2.2 Sample Collection

Sediment cores were collected using thin-walled aluminium core tube to minimize

compaction and then transferred into 15-cm i.d., 30-cm long thick-walled glass cores

before transporting to a greenhouse. Approximately 20-cm long sediment columns were

taken between the culms of Spartina alterniflora.

3.2.3 Testing Crude Oil

The ‘sweet’ South Louisiana crude oil (SLCO) was used in the present work. The

PAHs content of the SLCO was modified by adding pyrene and phenanthrene by about

0.2 g of each/ mL of crude oil. The modification of the testing oil chemical composition

was meant to increase the amount of the selected model PAHs to levels comparable to

other crude oil samples, since SLCO has its name 'sweet' for having relatively lower

amount of PAHs. Technically, the PAHs represent the more recalcitrant fraction of crude

oil, and therefore, this would help evaluate 'fairly' the biodegradation potential of crude

oil in the salt marshes.

17

Sampling site

Figure 3.1: Map of the study site located in the Leeville Oil Field

The degradation profiles of phenanthrene and pyrene are presented in Figure B1

(Appendix B). The crude oil was artificially weathered before spiking into the sediment

cores by flushing with nitrogen for about 48 hours to minimize the amount of volatile

hydrocarbon components so that oil loss due to volatilization is minimized during the

biodegradation study. A loss of about 15% of the initial crude oil weight was observed.

18

3.2.4 Experimentation

Fifteen cores (16-cm diameter x 35-cm long) were placed in a greenhouse and

each spiked with 35 mL (2 L/m2) of "sweet" Southern Louisiana Crude Oil (SLCO).

Cores were left for 7 days to initiate contact of oil with marsh soil. Crude oil was applied

directly to the surface of each core using a pipette. The air temperature of the greenhouse

was 22 ± 4oC during the experimental work. The cores were wrapped in aluminium foil

to prevent algae from growing below the soil surface on the sides of the cores. The cores

were subjected, in triplicate, to continuously-flooded (CF), intermittently-flooded (IF),

non-flooded (NF) regimes and control with no oil. The CF cores were flooded with sea-

water for the whole period of the experiments, having approximately a 10-cm deep-water

column. The IF cores were flooded with sea-water for 2 days and drained for 2 days,

alternately. The water was drained from the cores by siphoning with a small diameter

tube. The NF cores were left with water just flush with the sediment surface, only enough

to have the cores saturated. Water evaporation from both cores was compensated by

adding sea-water. Samples were taken after every 20 days.

Sediment samples were taken from the intact cores by scooping with a knife

approximately 5-cm deep, removing sediment sample weighing about 30-g. Then, the

sampled cavities in the cores were refilled with sand and marked to prevent resampling in

the same location. Care was taken not to sample subsequent samples too close from

previously sampled spots. Each sample was homogenized by manually mixing and

cutting with a serrated knife.

19

3.2.5 Extraction and Analysis of Crude Oil from Core Sediments

A 4-g sub-sample was apportioned from each sample collected from the intact

greenhouse cores. The 4-g sub-sample was placed in a Teflon tube, to limit adsorption of

any petroleum fraction, and 20 mL of a hexane: acetone solvent mixture (50/50 v/v%)

was added and the solution incubated on a shaker for 48 hours. After 48 hours, the

suspension was centrifuged at 3,000 rpm for about 20 minutes at room temperature. The

supernatant was transferred into a separatory funnel.

Using the separatory funnel, the petroleum-laden solvent was decanted into

scintillation vials, through sodium sulfate, to remove any remaining traces of water. The

petroleum-laden solvent was then evaporated to 5 mL using nitrogen gas to minimize

further oxidation. The samples were stored at 5oC until GC-MS analysis was performed.

Preparation for the GC-MS analysis included transferring 1-mL from the

scintillation vial into an amber GC-MS vial and adding 10-µL of internal standard (2000

µg/mL in methylene chloride containing the following components: 1,4-dichlorobenzene-

d4, naphthalene-d8, acenaphthene-d10, phenanthrene-d10, chrysene-d12 and perylene-d12)

(SUPELCO Chemical Co.). The sample was then analyzed on GC-MS using 17α(H),

21β(H)-hopane as a normalizing compound (i.e. ratio of compound to hopane

concentration), allowing only biodegradation to be monitored.

The analysis of the extracted hydrocarbon analytes was patterned after the US

EPA Method 8270 using GC-MS. A GC-MS (Hewlett Packard 5890 Series II Plus) was

utilized to analyze the samples for selected petroleum hydrocarbon components. The HP

5890 was outfitted with a HP-5 high-resolution capillary column (30-m x 0.250-mm i.d.,

0.25-µm film thickness) which was directly interfaced to a quadruple mass spectrometer

20

(HP 5972 Mass Selective Detector). The carrier gas was high purity helium at flow rate

of 1.0 mL/min, the injector temperature was 300 oC, and the column temperature was

300oC. The column temperature was programmed from 55 to 310oC at 8oC/min rate with

initial 3 minutes delay and 15 minutes hold at the end. The interface to the mass selective

detector was maintained at 280 oC.

Prior to sample analysis, a five-point calibration was established to demonstrate

the linear range of the analysis and to determine the relative response factors for

individual compounds.

Degradation data for alkanes and PAHs under the influence of flooding are

presented in Table B1 and B2 (Appendix B) respectively. The degradation data were

fitted using non-linear regression to the following first order kinetic equation:

kt

o

eCC −= (3.1)

where C = substrate's hopane ratio

Co= initial substrate's hopane ratio

k = first order rate constant (day-1)

t = time (days)

The half-lives of the crude oil fractions (i.e. alkanes (C10-C36) and PAHs) were

determined using the following relation for first-order kinetics:

k0.693

k2lnt

21 == (3.2)

21

where t1/2 = half life (days)

k = first order rate constant, (day-1)

The turnover times were determined using the following relationship:

k1(days)timeTurnover = (3.3)

where k = first order rate constant (day-1)

3.2.6 Microbial Activity Analysis: Fluorescein Diacetate (FDA) Assay

Fluorescein Diacetate (FDA) assay was used to quantify microbial activity in the

oil-contaminated sediment intact cores. The FDA assay has been used to measure total

heterotrophic soil microbial activity in a variety of ecosystems (Hunter, 2000; El-

Tarabily, 2002). The FDA assay does not quantify microbial biomass, but it is useful for

comparing microbial hydrolytic activity in similar soil ecosystems (Schnurer and

Rosswall, 1982).

The determination of FDA consists of incubating a soil sample in a buffer

solution in the presence of FDA, which acts as an electron acceptor that is reduced to a

coloured fluorescein, and the colour intensity is determined spectrophotometrically. The

amount of absorbance of fluorescein is indicative of the hydrolytic activity of the

heterotrophic microbial population within the soil sample. To obtain a constant

production rate of fluorescein and to avoid extensive growth of microorganisms, a short

incubation time of 1 hour is commonly used. Also, phosphate buffers are used to

minimize the influence of pH which exerts a significant effect on FDA hydrolytic

activity.

22

An FDA standard solution was made by dissolving 0.0399 g FDA in acetone and

bringing the volume to 100 mL. Standards were made by adding 50 mL phosphate buffer

and 10 g of each set of soil samples to each of seven flasks and then adding 0, 0.1, 0.2,

0.3, 0.5, 1.0 and 1.5 mL of fluorescein standard to the flasks. The resulting solutions

contained the equivalent of 0, 50, 100, 150, 250, 500 and 750 µg FDA converted to

fluorescein/flask. Standards were incubated on a rotary shaker (120 rpm) for 1 hour and

then 50 mL of acetone added. The solution was centrifuged for 10 minutes at 6000 rpm,

filtered and filtrate absorbance values were measured spectrophotometrically

(SHIMADZU UV-1201, 1-cm path length) at 490 nm. The absorbance values were

plotted to obtain a regression equation as presented in Figure A1 (Appendix A).

An FDA stock solution was made by dissolving 0.200 g fluorescein diacetate

(ALDRICH Chemical Co.) in acetone and bringing the volume to 100 mL with

deionized water. Ten grams of soil was weighed and placed in a Teflon tube. Then, 50

mL 0.1 M sodium phosphate buffer (pH 7.6) and 0.5 mL FDA stock solution was added

and the tube capped and incubated on a rotary shaker at 120 rpm for 1 hour. After one

hour, 50 mL acetone was added to terminate the FDA hydrolysis reaction. The solution

was swirled by hand and 40 mL decanted into a centrifuge tube. The solution was

centrifuged for about 10 minutes at 6000 rpm and then filtered (using 0.45 µm

polysulfone membrane filters) into scintillation vials and finally the filtrate absorbance

was measured spectrophotometrically (SHIMADZU UV-1201, 1-cm path length cell) at

490 nm. Absorbance values were converted to µg fluorescein produced/g soil/ hour by

using a standard absorbance curve created from a random selected oiled intact core

before the start of flooding.

23

3.2.7 Soluble Organic Carbon Analysis

One hundred mL of de-ionized water was added to 10-g moist soil sample and

then the solution was incubated on a shaker at 120 rpm for about 1 hour and allowed to

stand for approximately 18 hours (overnight). The solution was shaken by hand and 40

mL was poured into a centrifuge tube and centrifuged at 6000 rpm for 10 minutes.

Twenty mL of the supernatant was filtered through 0.45 µm polysulfone membrane filter

into a scintillation vial and refrigerated at 4 oC prior to analysis.

The four-point calibration of the TOC analyzer for SOC analysis was performed

using Potassium hydrogen phthalate (C8H5O4K) (SIGMA Chemical Co.). The calibration

curve is presented in Figure A2 (Appendix A).

Samples were analyzed for non-purgable organic carbon using a Total Carbon

Organic Analyzer (SHIMADZU TOC-5050A). Non-purgable organic carbon

concentration in each sample was measured by acidifying the sample with 40 µL of HCl

and then purging for 8 minutes with TOC grade compressed air. Acidification reduces

inorganic carbon to primarily CO2 in these samples and purging volatilizes CO2 out of

solution. Samples were then analyzed for soluble organic carbon (SOC) concentration.

Results were corrected for soil moisture so that the final results were expressed as mg

SOC/g soil on a dry weight basis.

3.2.8 Statistical Analyses

In both experiments three replicates per treatment were used. Data were analyzed

using SIGMASTAT® version 1.0. One way Analysis of variance (ANOVA) were

performed at the significance level of 5% to detect significant differences among the

24

flooding regimes. Both stepwise regression and Pearson correlation techniques were used

to determine significant linear relationships among the parameters measured.

3.3 Results

3.3.1 Biodegradation of Alkanes and PAHs

The residual alkane and PAH concentration profiles over time relative to hopane

were used to account for biodegradation and were used to detect statistically significant

effects of flooding regime. Degradation profiles for both alkanes (C10-C36) and PAHs are

presented in Figure 3.2 while the degradation data are presented in Table B1 and B2

(Appendix B) respectively. The alkanes (n-C10 to n-C36) decreased by 94.6%, 92.4%, and

90.9% in the NF, IF and CF regime, respectively whereas PAHs decreased by 87.8%,

78.6% and 75.6% in the NF, IF and CF regime, respectively. This demonstrates that

some biodegradation of the crude oil was occurring.

The degradation data were fitted to both zero-order and first-order kinetics to

confirm the common practice of using the first-order kinetics in fitting oil degradation

data, however, only the results for the former are presented in detail. This is because first-

order kinetics was found to fit the data better based on the correlation of coefficient (R2)

and some form of the coefficient of variation determined as 100x(k)constantRate

ErrorStandard

.

For the different flooding regimes, the zero-order kinetics had numerically lower

R2 values (from 0.82 to 0.94) for alkanes though statistically comparable (paired t-test; P

= 0.15) to those of first-order kinetics (from 0.96 to 0.99). Similarly, zero-order kinetics

had numerically higher coefficient of variation values (from 14.21% to 27.17%) for

25

alkanes though statistically comparable (paired t-test; P=0.210) to those of first-order

kinetics (from 4.76% to 14.81%).

The comparison of the PAHs indicated zero-order kinetics having statistically

comparable (paired t-test; P=0.184) R2 values (from 0.96 to 0.98) from those of first-

order kinetics (from 0.92 to 0.97). However, zero-order kinetics had numerically lower

coefficient of variation values (7.94% to 11.17%) though comparable (paired t-test;

P=0.057) to those of first-order kinetics (from 11.11% to 17.39%).

From the above results, on the basis of R2 values and coefficients of variation, it

was concluded that the degradation data were better fitting first-order kinetics.

No significant differences in biodegradation of crude oil were detected for both

among the flooding regimes except for CF regime against NF regime in terms of both

total alkanes (P=0.005) and total PAHs (P=0.02). The first order rate constants and other

statistical results are summarized in Table 3.1. The rate constants for NF regime were

significantly greater than the IF regime which are greater than CF cores. The results

corresponds to half–lives of 16.50, 20.39, 25.67 days for alkanes as compared to 30.14,

38.51 and 49.51 days for PAHs. Further, the degradation results correspond to turnover

times of 23.81, 47.62 and 62.50 for alkanes (n-C10 to n-C36) in comparison to 43.48,

55.56 and 71.43 days for PAHs. However, no significant differences (P = 0.095) were

detected among replicate cores for the same flooding regime. This reflects a satisfactory

reproducibility with individual treatments.

Also, it was determined from Figure 3.2 that, beyond day 60, there were no

significant differences in both residual alkanes (P=0.933) and PAHs (P=0.933) among the

flooding regimes.

26

3.3.2 Microbial Activity

The FDA hydrolytic activity assay was used to measure microbial activity,

determined as the rate of hydrolysis of FDA to fluorescein which was detected using

spectrophotometer at a wavelength of 490 nm. The time profile of microbial activity (the

amount of FDA hydrolyzed) and mean values for each flooding regime are presented in

Figure 3.3 while the data are presented in Table C1 (Appendix C). The mean FDA

hydrolyzed was about 15.43, 18.65, 22.67 and 24.73 µg FDA hyrolyzed/g dry soil/hr for

control, CF cores, IF cores and NF cores respectively. The amount of FDA hydrolyzed in

NF cores were found to range from 1.2 to 1.7 times those in CF cores. No significant

differences (P > 0.05) were detected in microbial activity among the flooding regimes

with the exception of NF cores against the control (with no oil with intermittent flooding

to mimic the tidal flooding) (P = 0.0393).

3.3.3 Soluble Organic Carbon

The time profile of SOC under the influence of flooding is presented in Figure 3.4

while the data are presented in Table D1 (Appendix D). The mean SOC values were

found to be 0.98, 1.38, 1.09 and 2.7 mg C/ g-oven-dry-soil for control, NF regime, IF

regime and CF regime respectively. The values of SOC for CF regime were consistently

higher than NF regime ranging from about 1.2 to 4 times. No significant differences

were detected among the flooding regimes, with the exception of IF regime against the

control and NF regime (P =2.62 x 10-4).

27

3.3.4 Regression and Correlation of Measured Parameters

The stepwise regression and Pearson correlation techniques were performed to

establish relationships between hydrocarbon hopane ratio, SOC concentration and

microbial activity (amount of FDA hydrolyzed).

A sample output for stepwise regression is presented in Appendix E while the P-values

for Pearson correlation are presented in Table F1 and F2 (Appendix F) for alkanes and

PAHs respectively. The analysis utilized data from the IF regime only since this regime

mimic the tidal flooding typically experienced by salt marshes. Both stepwise regression

and Pearson correlation techniques revealed no significant linear relationships (P > 0.05)

among these variables for both alkanes and PAHs. This suggests complex interaction

between soil geochemical and microbiological properties in determining the significance

of biodegradation of crude oil in salt marshes at least under the experimental conditions.

3.4 Discussion

Significant differences were detected in biodegradation of crude oil fractions

among the flooding regimes. The non-flooded (NF) regime had the highest inherent

biodegradation rates compared to intermittently-flooded (IF) and continuously-flooded

(CF) regime, in that order. These results contradict those previously reported by Wright

et al (1997), in which it was reported that the CF regime had higher inherent

biodegradation rate than the IF regime. The discrepancy between the two studies may be

attributed to nutrient amendment used in the latter study, which may have lead to

difference in microbial diversity and associated metabolic activity.

28

Time (days)

0 20 40 60 80

Tot

al A

lkan

e H

opan

e ra

tio

0

20

40

60

80

100

120

Continuously - Flooded (CF) regimeIntermittently - Flooded (IF) regimeNon - Flooded (NF) regime

(a) Alkane (C10-C36) degradation profile

Time (days)0 20 40 60 80

Tot

al P

AH

Hop

ane

ratio

0

5

10

15

20

25

30

Continously - Flooded (CF) regimeIntermittently - Flooded (IF) regimeNon - Flooded (NF) regime

(b) PAH degradation profile

Figure 3.2: The effect of flooding regime on selected residual petroleum hydrocarbons

29

Table 3.1: Summary results of first-order rate constants for alkanes and PAHs under the influence of flooding

Alkanes (C10-C36) PAHs

Treatment k

(day-1)

Std

error

t1/2

(days)

Turnover

time

(days)

R2

k

(day-1)

Std

error

t1/2

(days)

Turnover

time (days)

R2

Non-flooded (NF) 0.042 0.002 16.50 23.81 0.99 0.023 0.004 30.14 43.48 0.95

Intermittently-flooded (IF) 0.021 0.002 33.01

47.62 0.99 0.018 0.002 38.51 55.56 0.97

Continously-flooded (CF) 0.016 0.004 43.32 62.50 0.96 0.014 0.002 49.51 71.43 0.92

30

Time elapsed after the last oil spike (days)

0 20 40 60 80

ug F

DA

hyd

roly

zed/

g so

il/ h

our

10

20

30

40 Control Continously-Flooded (CF) regimeIntermittently-Flooded (IF) regimeNon-Flooded (NF) regime

(a) Microbial activity time profile

Figure 3.3: The effect of flooding on microbial activity in terms of the amount of FDA hydrolyzed

31

Time elapsed (days)

0 20 40 60 80

SOC

(mg

C/g

ove

n dr

y so

il)

0

2

4

6

8

10Control Non-Flooded (NF) regimeIntermittently-Flooded (IF) regimeContinously-Flooded (CF) regime

(a) SOC time profile

Figure 3.4: The effect of flooding on soluble organic carbon (SOC) content

32

Since there was poor correlation between SOC concentration against soil

microbial activity and biodegradation of crude oil, this suggest that other factors could

account for the lower biodegradation observed under flooding conditions possibly oxygen

limitation. It is known that, tidal flooding in salt marshes may prevent oxygen from

diffusing to the soil, increase nutrient inputs, dilute salinities and may well affect the soil

pH (Taylor III, 1995). Lack of oxygen to support aerobic metabolism is probably one of

the important factor that influences microbial activity and this may affect biodegradation

of crude oil under flooding conditions. This is based on the assumption that the NF cores

had higher oxygen availability (redox potential) due to more oxygen being able to reach

the soil, which is more exposed to the air as opposed to IF cores and CF cores

characterized by a water column over the soil surface. The highest redox potentials in

wetland soils are found in the top 0-2 cm layer (Taylor III, 1995; Shin, 1998), therefore,

this is the location where oil biodegradation is expected to occur faster, but less oxygen is

available during a flooding event. It is estimated that, with water as the oxygen carrier

from the air to the soil, air will supply about 8 mg O2/L of water and about 400 kg of

water would be required to degrade 1 g of hydrocarbon (Riser-Roberts, 1998).

Alternatively, Johnston (1970) estimated that amounts of oil greater than 100 g/m2 (with

about 2000 g oil/m2 in the present work) would initiate the onset of anaerobic conditions.

This indicates that oxygen might have been limited during flooding of the intact cores,

with anaerobic conditions prevailing.

The preceding discussion introduces the role of anaerobic biodegradation of crude

oil in the natural recovery of oil-contaminated salt marsh intact cores. The significance of

the anaerobic degradation of petroleum hydrocarbons was previously thought to be slow

33

as to be negligible or not to occur at all (Shelton and Hunter, 1975) since oxygen was

assumed to naturally diffuse from the atmosphere into the soil during periods of low

tides. However, several studies have established the significance of anaerobic

biodegradation of petroleum hydrocarbons (Hambrick, 1979; Berry et al, 1987; Coates et

al, 1996; Phelps and Young, 1999; Rockne et al, 2000). In a recent study, Pardue et al

(2001) observed increase in sediment oxygen demand (from 2,000 to 11,000 mg O2/m2-

day) and sulfate reduction rate (indicator of anaerobic conditions in salt marshes) (from

~2000 to 4,000 mg SO42-/m2-day) following an experimental crude oil spill (1.42 L/m2).

This demonstrates the importance of both aerobic and anaerobic processes during natural

recovery of an oiled salt marsh. Therefore, the degradation of the crude oil components,

can no longer be considered a defining characteristic of aerobic biodegradation processes

alone (Caldwell et al, 1998), and particularly so in the coastal marshes, with the spilled

oil trapped in the essentially anoxic sediments.

The higher microbial activity and biodegradation of crude oil in NF regime may

be related to possible difference in microbial population among the flooding regimes as

suggested by the FDA assay. A case in point, De and Bose (1938) found that bacterial

and fungal numbers were markedly reduced on flooding rice soil and that the decrease

became more pronounced with time under laboratory conditions. This may be explained

of the fact that in non-flooded soils, a wide range of microorganisms assisted by the

microfauna of the soil participates in organic matter and nutrient mineralization as

opposed to flooded soils involving mostly anaerobic bacteria (Christian and Wiebe,

1978). Therefore, the processes of both decomposition and assimilation of organic matter

and nutrient mobilization in flooded soils are comparatively much slower. In addition,

34

decomposition of organic matter and nutrient mobilization in flooded soils differ from

those in non-flooded soils due to differences in the decomposition end-products

(Gambrell and Patrick Jr., 1979). Typically, ethanol and hydrogen sulfide are commonly

produced in flooded marsh soils (Riser-Roberts, 1998) which can be toxic to some

microbial species, resulting in slowed overall microbial activity.

Further, higher biodegradation rate and microbial activity in NF cores may have

been facilitated by attachement of the petroleum hydrocarbons onto the soil sediments

which assist their availability to microbes (de Jonge et al, 1997; Carlsson, 1998). The

adsorption of crude oil components onto the salt marsh sediments may be of significance

as it has been observed that generally soil-attached bacteria are 2-3 times more

metabolically active than the freely-suspended water column bacteria (Nyman, 1999).

Though not well understood, it has been proposed that once bacteria become attached,

microcolonies and associated extracellular material grow on the particle surface forming

a biofilm.

The inherent lower biodegradation rate of crude oil in CF cores may as well be

ascribed to changes in soil biogeochemistry such as sediment-water mineral exchange. In

unstirred flooded sediment cores spiked with crude oil (0-30 L/m2) over 5-cm overlying

water column, DeLaune et al (1979) observed the release of iron, manganese and

ammonium ions from salt marsh sediments to the overlying water column possibly due

to lack of oxygen because of the oil barrier on the water surface. In addition, it is believed

that reduced soils and sediments result in different degradative microbial populations and

breakdown pathways of organic pollutants and possibly different behavior in adsorption

to soil and sediment solids (Gambrell and Patrick Jr., 1979).

35

The decomposition of organic matter was monitored by measuring the soluble

organic carbon (SOC) concentration. The SOC represents the fraction of organic

compounds in a soil matrix that is water soluble at room temperature and is mainly

comprised of sugars, amino acids, fulvic and humic acids, that are readily utilized by

microbes (Hunter, 2000). Significant differences were observed in SOC values among

flooding regime, with comparatively higher SOC values observed in CF cores. However,

it was hypothesized that lower amounts of SOC would exist in CF regime as compared to

NF regime since SOC production is dependent on microbial population and metabolic

activity in the soil. In support of this hypothesis, the rate of decomposition of organic

matter in flooded soils is generally estimated to be only about half that in non-flooded

soil (Riser-Roberts, 1998). Therefore, the observation made in this study raises an

important question: why did CF cores contain comparatively higher SOC level than other

flooding regimes despite showing lower microbial activity? This may be attributed to

changes in solution chemistry or starvation and lysis of microbes possibly due to

substrate depletion which can contribute to SOC release from the sediments (Marschner

and Bredow, 2002).

Alternatively, the relatively higher SOC content observed in the CF regime may

be related to distribution and inherent microbial activity between sediment-attached and

freely-suspended microbial population. As a guideline, Bekins et al (1999) determined

that in an anaerobic portion of an aquifer contaminated by crude oil, only about 15% of

the total microbial population were freely-suspended. In addition, it is known that

microbes attached to soil sediments are associated with higher growth rates as compared

to those in the water column since nutrients tend to concentrate at sediment surfaces

36

(Carlsson, 1998), therefore, resulting into accumulation of SOC. Otherwise, the relatively

high SOC concentration in CF regime can be interpreted as a result of lower consumption

of the produced SOC supposedly due to lower microbial activity as indicated by the

fluorescein diacetate (FDA) assay.

3.5 Conclusions and Implications

Tidal flooding is of ecological significance in salt marshes, and therefore may

have an important role on the physical and functional recovery of the salt marshes

following oil spills. The effect of batch-flooding - continuously-flooded (CF),

intermittently-flooded (IF) and non-flooded (NF) regimes - was investigated using salt

marshes intact cores growing Spartina Alterniflora spiked with south Louisiana crude oil

(2 L/m2). Residual petroleum hydrocarbons, heterotrophic microbial activity and soluble

organic carbon (SOC) were monitored for about 3 months.

The biodegradation rate of both alkanes (n-C10 to n-C36) and PAHs and microbial

activity essentially increased in the order from the CF regime, IF regime to NF regime.

The SOC concentration increased significantly (P < 0.05) in the opposite order. Both

stepwise regression and Pearson correlation revealed no significant linear relationships (P

> 0.05) among the parameters investigated, suggesting for complex interaction among the

measured parameters in predicting the fate of spilled oil in salt marshes at least under the

experimental conditions.

The results from this work suggest that the pattern and frequency of flooding

markedly influenced the biodegradation of crude oil, heterotrophic microbial activity and

SOC concentration. This reflects that tidal flooding is not only important in terms of

ecological function of salt marshes but also has a key role in determining the nature and

37

level of microbial activity and indirectly affects the biodegradation of spilled oil and

decomposition and accumulation of organic matter.

38

CHAPTER 4

OIL SPILL RECURRENCE IN A SALT MARSH UNDER NATURAL RECOVERY

4.1 Introduction

Crude oil spills in the marine environment is one of the major pollution problems

in the United States and worldwide (Jackson, 1996; Shin, 1998; Wright et al, 1997). It is

estimated that world annual oil spills into the ocean amounts in the range between 1.7 and

8.8 million metric tons, equivalent to about 0.1 to 0.2 % of the world annual petroleum

production (National Academy of Sciences, 1985; Harayama et al, 1999). Oil spills

involve numerous spill sources including oil-shipping tankers after accidents, oil

exploration and development activities, rupture or leakage from oil pipelines laid through

the ocean, and even natural seeps. Oil is swept into salt marshes by tidal currents and

wind and is trapped by marsh grass and the organic-rich sediments. The intermittent

nature of the tidal flooding reflects the potential for repetitive spillage of the salt marshes

and the consequences in terms of biodegradation potential can be important in assessing

the environmental impact. Comparatively, much less is known as to the intrinsic

biodegradation potential of complex mixture of petroleum hydrocarbons such as crude oil

in marine such as the salt marshes following oil spill recurrence.

A vast majority of available literature suggest that, the fate of petroleum

hydrocarbons in the environment is dependent on the characteristics and pollution history

of the sediments (Leahy and Colwell, 1990; Freedman, 1995). For instance, Hayes et al

(1999) observed that naphthalene and phenanthrene were oxidized without a lag phase in

marine harbor sediments that were previously contaminated with petroleum while pristine

39

sediments showed no significant degradation. In a parallel effort, Phelps and Young

(1999) examined the extent of biodegradation of BTEX (benzene, toluene, ethylbenzene,

and xylenes) as a mixture and from gasoline in pristine and previously polluted marine

harbor sediments. Similarly, higher biodegradation rates were observed for the previously

polluted sediments. On the other hand, Edwards and Grbic-Galic (1994) observed

inhibition of degradation of toluene and o-xylene under anaerobic conditions (lag phase

ranging between 100-255 days). This was presumed to be due to presence of natural

organic substrates or contaminants in the sediments that were collected from historically-

known contaminated sites. Reflecting on these studies, a question emerges about the

effect of oil spill recurrence on the natural restorative ability of the salt marshes

especially of a complex mixture of petroleum hydrocarbons such as crude oil.

There are a handful of laboratory-based studies that have assessed the effect of

repeated application of crude oil in salt marshes with emphasis on recovery of vegetative

plants (Baker, 1973; Li et al, 1990) and microbial activity in terms of CO2 production

(total respiration), acetylene reduction activity, denitrification and methanogenesis (Li et

al, 1990). Noteworthy, the focus of these studies was to determine the levels of oil that

can impair the selected marsh ecosystem receptors, therefore, different amounts of crude

oil were compared among the treatments. That is to say, these results can only provide

qualitative information as to the biodegradation potential of crude oil in salt marshes

following the oil spill recurrence.

While microbial degradation is an important fate process for the trapped oil in salt

marsh sediments, it is affected by soil chemical, physical an biological factors. In that

perspective, the present work examined residual petroleum hydrocarbons, soil

40

heterotrophic microbial activity and soluble organic carbon to determine the influence of

oil spill recurrence on the fate of an experimental crude oil spill (2 L/m2) in salt marsh

intact cores. Specifically, single oiling was compared to multiple successive oiling for

the same total volume of crude oil.

4.2 Materials and Methods

4.2.1 Site Description

The study site is located near Port Fourchon at the southwestern end of the

Barataria Basin in Louisiana. While catering to several other business sectors, the

primary purpose of the port is to support offshore oil and gas activities throughout the

central Gulf of Mexico. The site is situated in the Leeville oil field in the Lafourche

Parish at approximately 29o14’ 52” N latitude and 90o 12’ 27’W longitude. The climate is

sub-tropical, with annual temperature averaging 15oC with a mean annual low of 10 oC

and a mean annual high of 30 oC. Average yearly precipitation is about 157 cm/year.

The marsh site is flooded with diurnal tides of approximately 0.07-0.67 m in

magnitude, which are predominantly influenced by seasonal winds. The marsh site is

dominated by uniform stands of Spartina alterniflora plants.

4.2.2 Sample Collection

Sediment cores were collected using thin-walled aluminium core tube to minimize

compaction and then transferred into 15-cm i.d., 30-cm long thick-walled glass cores

before transporting to the laboratory. Approximately 20-cm long sediment columns were

taken between the culms of Spartina alterniflora.

41

4.2.3 Testing Crude Oil

The ‘sweet’ South Louisiana crude oil (SCLO) was used in the present work. The

PAHs content of the SLCO was modified by adding pyrene and phenanthrene for about

0.2 g of each compound /mL of crude oil. The modification of the testing oil chemical

composition was meant to increase the amount of the selected model PAHs to levels

comparable to other crude oil samples, since SLCO has its name 'sweet' for having

relatively lower amount of PAHs. Technically, the PAHs represent the more recalcitrant

fraction of crude oil, and therefore, this would help evaluate 'fairly' the biodegradation

potential of crude oil in the salt marshes. The degradation profiles of phenanthrene and

pyrene are presented in Figure B2 (Appendix B). The crude oil was artificially weathered

before spiking into the sediment cores by flushing with dry nitrogen gas for about 48

hours to minimize the amount of volatile hydrocarbon components so that oil loss due to

volatilization is minimized during the biodegradation study. A loss of about 15% of the

initial crude oil weight was observed.

4.2.4 Experimentation

A total of fifteen sediment intact cores (16-cm diameter x 35-cm long) were set up

(in triplicate for each treatment) as follows:

•

•

•

•

•

Treatment 1: Single oil spike of 40 mL

Treatment 2: Two oilings @ 20 mL oil , at 5-day interval

Treatment 3: Three oilings @ 13.3 mL oil, at 5-day intervals

Treatment 4: Four oilings @ 10 mL, at 5-day intervals

Treatment 5: Control with no oil

42

Artificially weathered crude was applied directly to the surface of each core (2

L/m2) using a pipette as per treatment shown above. The cores were wrapped with

aluminium foil to avoid light penetration and growth of algae. After 25 days (5 days after

the last spike in treatment 4), the cores were flooded for 2 days and drained for 2 days

alternately, to mimic the tidal effect. The water was drained from the cores by siphoning

with a small diameter tube. Water evaporation from cores was compensated by adding

seawater. Samples were taken after every 20 days.

Sediment samples were taken from the intact cores by scooping with a knife

(about 5-cm deep), removing approximately 30-g samples Then, the sampled cavities in

the cores were refilled with sand and marked to show previously sampled locations. Care

was taken not to sample subsequent samples too close from previously sampled spots.

4.2.5 Extraction and Analysis of Crude Oil from Core Sediments

A 4-g sub-sample was apportioned from each sample collected from the intact

greenhouse cores. The 4-g sub-sample was placed in a Teflon tube, to limit adsorption of

any petroleum fraction, and 20 mL of a hexane: acetone solvent mixture (50/50 v/v %)

was added and the solution incubated on a shaker for 48 hours. After 48 hours, the

suspension was centrifuged at 3,000 rpm for about 20 minutes at room temperature. The

supernatant was transferred into a separatory funnel.

Using the separatory funnel, the petroleum-laden solvent was decanted into

scintillation vials, through sodium sulfate, to remove any remaining traces of water. The

petroleum-laden solvent was then evaporated to 5 mL using nitrogen gas to minimize

further oxidation. The samples were stored at 5oC until GC-MS analysis was performed.

43

Preparation for the GC-MS analysis included transferring 1-mL from the

scintillation vial into an amber GC-MS vial and adding 10-µL of internal standard (2000

µg/mL in methylene chloride containing the following components: 1,4-dichlorobenzene-

d4, naphthalene-d8, acenaphthene-d10, phenanthrene-d10, chrysene-d12 and perylene-d12)

(SUPELCO Chemical Co.). The sample was then analyzed on GC-MS using 17α(H),

21β(H)-hopane as a normalizing compound (i.e. ratio of compound to hopane

concentration), allowing only biodegradation to be monitored.

The analysis of the extracted hydrocarbon analytes was patterned after the US

EPA Method 8270 using GC-MS. A GC-MS (Hewlett Packard 5890 Series II Plus) was

utilized to analyze the samples for selected petroleum hydrocarbon components. The HP

5890 was outfitted with a HP-5 high-resolution capillary column (30-m x 0.250-mm i.d.,

0.25-µm film thickness) which was directly interfaced to a quadruple mass spectrometer

(HP 5972 Mass Selective Detector). The carrier gas was high purity helium at flow rate

of 1.0 mL/min, the injector temperature was 300 oC, and the column temperature was

300oC. The column temperature was programmed from 55 to 310oC at 8oC/min rate with

initial 3 minutes delay and 15 minutes hold at the end. The interface to the mass selective

detector was maintained at 280 oC.

Prior to sample analysis, a five-point calibration was established to demonstrate

the linear range of the analysis and to determine the relative response factors for

individual compounds.

The degradation data for alkanes and PAHs are presented in Table B3 and B4

(Appendix B) respectively. The degradation data were fitted using non-linear regression

to the following first order kinetic equation:

44

kt

o

eCC −= (4.1)

where C = substrate's hopane ratio

Co= initial substrate's hopane ratio

k = first order rate constant, day-1

t = time, days

The half-lives of the crude oil fractions (i.e. alkanes and PAHs) were determined

using the following relation for first-order kinetics:

k0.693

k2ln

t2

1 == (4.2)

where t1/2 = half life (days)

k = first order rate constant, (day-1)

The turnover times were determined using the following relationship:

k1(days)timeTurnover = (4.3)

where k = first order rate constant (day-1)

4.2.6 Microbial Activity Analysis: Fluorescein Diacetate (FDA) Assay

Fluorescein Diacetate (FDA) assay was used to quantify microbial activity in the

oil-contaminated sediment intact cores. The FDA assay has been used to measure total

45

heterotrophic soil microbial activity in a variety of ecosystems (Hunter, 2000; El-

Tarabily, 2002). The FDA assay does not quantify microbial biomass, but it is useful for

comparing microbial hydrolytic activity in similar soil ecosystems (Schnurer and

Rosswall, 1982).

The determination of FDA consists of incubating a soil sample in a buffer

solution in the presence of FDA, which acts as an electron acceptor that is reduced to a

coloured fluorescein, and the colour intensity is determined spectrophotometrically. The

amount of absorbance of fluorescein is indicative of the hydrolytic activity of the

heterotrophic microbial population within the soil sample. To obtain a constant

production rate of fluorescein and to avoid extensive growth of microorganisms, a short

incubation time of 1 hour is commonly used. Also, phosphate buffers are used to

minimize the influence of pH which exerts a significant effect on FDA hydrolytic

activity.

An FDA standard solution was made by dissolving 0.0399 g FDA in acetone and

bringing the volume to 100 mL. Standards were made by adding 50 mL phosphate buffer

and 10 g of each set of soil samples to each of seven flasks and then adding 0, 0.1, 0.2,

0.3, 0.5, 1.0 and 1.5 mL of fluorescein standard to the flasks. The resulting solutions

contained the equivalent of 0, 50, 100, 150, 250, 500 and 750 µg FDA converted to

fluorescein/flask. Standards were incubated on a rotary shaker (120 rpm) for 1 hour and

then 50 mL of acetone added. The solution was centrifuged for 10 minutes at 6000 rpm,

filtered and filtrate absorbance values were measured spectrophotometrically

(SHIMADZU UV-1201, 1-cm path length cell) at 490 nm. The absorbance values were

plotted to obtain a regression equation as shown in Figure A1 (Appendix A).

46

An FDA stock solution was made by dissolving 0.200 g fluorescein diacetate

(ALDRICH Chemical Co.) in acetone and bringing the volume to 100 mL with

deionized water. Ten grams of soil from each sample was weighed and placed in a teflon

tube. Then, 50 mL 0.1 M sodium phosphate buffer (pH 7.6) and 0.5 mL FDA stock

solution was added and the tube was capped and incubated on a rotary shaker at 120 rpm

for 1 hour. After one hour, 50 mL acetone was added to terminate the FDA hydrolysis

reaction. The solution was swirled by hand and 40 mL decanted into a centrifuge tube.

The solution was centrifuged for about 10 minutes at 6000 rpm, filtered (using 0.45 µm

polysulfone membrane filters) and filtrate absorbance was measured

spectrophotometrically (SHIMADZU UV-1201, 1-cm path length cell) at 490 nm.

Absorbance values were converted to µg fluorescein produced/g soil/ hour by using a

standard absorbance curve created from a selected oiled core before the start of flooding.

4.2.7 Soluble Organic Carbon Analysis

One hundred mL of deionized water was added to 10-g moist soil from each

sample and the solution was shaken at 120 rpm for 1-hour and allowed to stand for

approximately 18 hours (overnight). The solution was shaken by hand and 40 mL was

poured into a centrifuge tube and centrifuged at 6000 rpm for 10 minutes. Twenty mL of

the supernatant was filtered through 0.45-µm polysulfone membrane filter into a

scintillation vial and refrigerated at 4 oC prior to analysis.

The four-point calibration of the TOC analyzer for SOC analysis was performed

using Potassium hydrogen phthalate (C8H5O4K) (SIGMA Chemical Co.). The calibration

curve is presented in Figure A2 (Appendix A).

47

Samples were analyzed for nonpurgable organic carbon using a Total Organic

Analyzer (SHIMADZU TOC-5000A). Nonpurgable organic carbon concentration in each

sample was measured by acidifying the sample with 40 µL of HCl and then purging for 8

minutes with TOC grade compressed air. Acidification reduces inorganic carbon to

primarily CO2 in these samples and purging volatilizes CO2 out of solution. Samples

were then analyzed for organic carbon concentration. Results were corrected for soil

moisture so that the final results were expressed as mg SOC/g soil on a dry weight basis.

4.2.8 Statistical Analyses

In both experiments three replicates per treatment were used. Data were analyzed

using SIGMASTAT® version 1.0. Analysis of variance (ANOVA) at significance level of

5% was used to detect significant differences among the oil spill recurrence treatments.

Stepwise regression and Pearson techniques were used to determine linear relationships

among the parameters measured.

4.3 Results

4.3.1 Biodegradation Potential of Alkanes and PAHs

The residual alkanes (n-C10 to n-C36) and PAHs concentrations were monitored

and the results are normalized with hopane concentration to account for biodegradation

only. The degradation profiles for both alkanes and PAHs are presented in Figure 4.1

while the degradation data are presented in Table B3 and B4 (Appendix B). The alkanes

decreased by 88.4%, 89.1%, 94.8 and 95.5% for single, two, three and four oilings,

respectively whereas PAHs decreased by 60.3%, 63.9%, 75.9% and 85.2% for the single,

two, three and four successive oilings, respectively.

48

The degradation data were fitted to both zero-order and first-order kinetics to

confirm the common practice of fitting oil degradation data to first-order kinetics,

however, only the results of the former are presented and in detail. This is because first-

order kinetics was found to fit the data better based on the correlation of coefficient (R2)

and some form of coefficients of variation determined as 100x(k)constantRate

ErrorStandard

.

For the different spill recurrence treatments, the zero-order kinetics for had

statistically significant lower (paired t-test; P=0.001) R2 values (from 0.70 to 0.78) for

alkanes from those of first-order kinetics (from 0.98 to 0.99). Similarly, zero-order

kinetics had statistically significant higher (paired t-test; P=0.004) coefficients of

variation (from 31.11% to 37.54%) for alkanes from those of first-order kinetics (from

6.90% to 13.16%).

The comparison of the PAHs indicated zero-order kinetics having statistically

comparable (paired t-test; P=0.532) R2 values (from 0.85 to 0.98) from those of first-

order kinetics (from 0.95 to 0.97). However, zero-order kinetics had numerically higher

coefficient of variation values (7.83% to 24.27%) though statistically insignificant (paired

t-test; P=0.383) from those of first-order kinetics (10.00% to 13.64%).

In view of the above results, on the basis of R2 and coefficient of variation values,

it was concluded that the oil degradation data were better fitting first-order kinetics.

Significant differences (P = 0.0002) were detected in biodegradation rate of crude

oil among the spill recurrence treatments with the exception of single against two

successive oilings as well as three against four successive oilings. The first order rate

constants of both alkanes (from n-C10 to n-C36) and PAHs under oil spill recurrence

49

treatment and other statistical results are summarized in Table 4.1. The rate constants

essentially increased in the order from single oiling to four successive oilings. The results

correspond to half-lives of 18.24, 17.77, 13.59 and 11.95 days for alkanes as compared to

69.31, 69.31, 38.51, 31.51 days for PAHs. Further, these results correspond to turnover

times of 26.32, 25.64, 19.61 and 17.24 days for alkanes as compared to 100.00, 100.00,

55.56 and 45.45 days for PAHs.

4.3.2 Microbial Activity

The profile of microbial activity (as expressed in terms of the amount of FDA

hydrolyzed) under the influence of oil spill recurrence is presented in Figure 4.2 while the

data are presented in Table C2 (Appendix C). The mean values of the microbial activity

were obtained as 12.76, 30.08, 34.75 and 15.43 µg FDA hydrolyzed/ g-soil/hr for single

oiling, two oilings, four oilings and control. The amount of FDA hydrolyzed was

consistently higher for four oilings as compared to single oiling ranging from about 1.9 to

3 times. Significant differences (P = 2.25 x 10-9) in microbial activity (FDA hydrolyzed)

were detected among the oil spill recurrence treatments with the exception of the single

oiling against the control treatment (P > 0.05).

4.3.3 Soluble Organic Carbon (SOC)

The profile of SOC concentration under the influence of oil spill recurrence is

presented in Figure 4.3 while the data are presented in Table D2 (Appendix D). The mean

SOC concentrations were 1.40, 1.63, 1.83 and 0.98 mg C/g-oven-dry-soil for single

oiling, two oilings, four oilings and control treatments respectively. The SOC values for

the four oilings treatment were consistently higher than single oiling ranging from about

50

1.2 to 1.5 times. Significant differences (P = 0.00028) in SOC concentration were

detected among the oiling treatments with the exception of four oilings against single and

two oilings (P > 0.05).

4.3.4 Regression and Correlation of Measured Parameters

Stepwise regression was conducted using data from the four-oiling treatment,

which had the highest degradation of crude oil and microbial activity, suggesting

existence of relatively minimal or no limitation in nutrients and/or other microbial growth

factors. Both stepwise regression and Pearson correlation were performed to determine

linear relationships among hydrocarbon hopane ratio, SOC concentration and microbial

activity (amount of FDA hydrolyzed). A sample output for stepwise regression is

presented in Appendix E while the P-values for Pearson correlation are presented in

Table F3 and F4 (Appendix F) for alkanes and PAHs respectively. Both techniques

revealed no significant linear relationships (P>0.05) among the measured parameters for

both alkanes and PAHs. This suggests complex interactions between soil biogeochemical

and microbiological properties in determining the significance of increased

biodegradation of crude oil in salt marshes at least under the experimental conditions.

4.4 Discussion

The results indicated that there were significant differences in biodegradation of

crude oil among the spill recurrence treatments with the exception of single against two

oilings as well as three against four oilings. The results demonstrate that biodegradation

of crude oil essentially increased with each subsequent oiling.

51

Time after the last (fourth) oil spike (days)

0 20 40 60 80

Tot

al A

lkan

e H

opan

e R

atio

0

20

40

60

80

Single oilingTwo successive oilingsThree succesive oilingsFour successive oilings

(a) Alkane (C10-C36) degradation profile

Time after the last (fourth) oil spike (days)

0 20 40 60 80

Tot

al P

AH

Hop

ane

ratio

0

10

20

30

40

Single oilingTwo successive oilingsThree successive oilingsFour successive oilings

(b) PAH degradation profile

.

Figure 4.1: The effect of oil spill recurrence on residual petroleum hydrocarbons

52

Table 4.1: Summary results of first-order rate constants of alkanes and PAHs following oil spill recurrence

Alkanes (n-C10 to n-C36) PAHs

Treatment k

(day-1)

Std

error

t1/2

(days)

Turnover

time (days)

R2

k

(day-1)

Std

error

t1/2

(days)

Turnover

time (days)

R2

Single oiling 0.038 0.005 18.24 26.32 0.98 0.010 0.001 69.31 100.00 0.95

Two successive oilings 0.039 0.005 17.77 25.64 0.98 0.010 0.001 69.31 100.00 0.95

Three successive oilings 0.051 0.004 13.59 19.61 0.99 0.018 0.002 38.51 55.56 0.97

Four successive oilings 0.058 0.004 11.95 17.24 0.99 0.022 0.003 31.51 45.45 0.96

53

Time elapsed after the last oil spike (days)

0 20 40 60 80

ug F

DA

hyd

roly

zed/

g so

il/hr

0

10

20

30

40

50

60 Single oilingTwo successive oilingsFour successive oilingsControl

(a) Microbial activity time profile

Figure 4.2: The effect of oil spill recurrence on microbial activity in terms of the amount of FDA hydrolyzed

54

Time elapsed after the last (fourth) spike (days)

0 20 40 60 80

SOC

(m

g C

/g-o

ven-

dry-

soil)

1

2

3 Single oilingTwo successive oilingsFour successive oilingsControl

(a) SOC time profile

Figure 4.3: The effect of oil spill recurrence on SOC concentration

55

The possible hypothesis that might explain the effect of subsequent oil application

is that, the increased degradation rate is primarily due to microbial adaptation resulting

into stimulatory effect on microbial metabolic activity and possibly, an increase in

number of microorganisms on each subsequent oiling. On the other hand, there is a

possibility for increase in metabolic activity per cell upon repeated exposure to crude oil

rather than a substantial increase in number of microbes. For instance, Al-Hadhrami et al

(1996) observed that, separate additions of surfactants and molasses resulted in

significant biodegradation of the n-alkane fraction of crude oil, however, there were no

significant differences in bacterial counts at the end of the experiments from those at the

beginning. It can be seen that further work is needed to explore the linkage between

microbial metabolic activity and growth aspects associated with oil spill recurrence.

Alternatively, the higher biodegradation rate and microbial activity observed for

three and four oilings may be related to shift in importance of one metabolic pathway

over another possibly as a result of microbial adaptation and/or competition. This is based

on the assumption that, more than one hydrocarbon compound degradation pathway

exists in different microbial species and therefore, it is possible that individual bacteria

able to degrade more than one aromatic substrate will have more than one pathway for

their metabolism (Stringfellow and Aitken, 1995).

The results indicated a relatively lower biodegradation rate and microbial activity

in single and two oilings suggesting that the slowed biodegradation may be due to

adverse impact from relatively higher initial loading of crude oil. This is important in

view of the microbial survival and adaptation in terms of suppressing effect on the

synthesis of enzymes involved in crude oil metabolism or by changes in the genetic

56

capacity of microbial species to maintain their ability to degrade crude oil (Leahy and

Colwell, 1990). For instance, Long et al (1995) observed that PAHs exerted toxic effects

on the active microbial community at concentrations above their solubility concentrations

while they noticed enrichment of specific degraders at their (PAHs) solubility

concentration levels. In relation to this, Leahy and Colwell (1990) in their review paper

cited that, microbial activities were generally enhanced in a contaminated soil containing

up to 5% hydrocarbon mass per dry weight of soil while oil concentrations over 10% may

result in inhibition of microbial activity by toxic components and/or by-products of the

oil. However, comparison to the present work may be difficult using the oil-to-soil ratio

as this may mislead on the extent of pollution in the intact sediment cores used, since the

spiked oil is believed hardly to have penetrated beyond the top 10-cm.

The lower biodegradation rate in the single and two oilings may be related to

competition between sulfate reducing microbes and methanogens in the salt marsh soil.

Vroblesky et al (1996) observed that when BTEX concentration was low in a

contaminated aquifer, sulfate reduction microbes outcompeted methanogenic microbes

for the available BTEX at a lower concentration of sulfate (< 1 mg/L) than when BTEX

concentration was higher. Although sulfate measurements were not taken in this study,

the relatively higher initial crude oil loading for single and two oilings presumably may

have limited sulfate reduction, which is known to be linked to biodegradation of crude oil

in salt marshes, therefore resulting in relatively lower biodegradation rate. Alternatively,

the lower biodegradation of crude oil in single and two oilings may be related to growth

of competing microbial population incapable of degrading crude oil but which deprives

57

the oil-degrading population of nutrients or else other growth factors may also be

involved.

On the other hand, the increased biodegradation rates and microbial activity

observed in the order from single to four oilings may be related to altered sorption

potential with each subsequent oiling. In other words, the biodegradation was controlled

by the desorption rate of the sorbed fraction of the petroleum hydrocarbons presuming

that the subsequent oilings had the role of “conditioning” the marsh soil.

The results obtained in this work can be explained in terms of competitive

inhibitory and enhancement effect among the petroleum hydrocarbons within the crude

oil as a function of concentration. In a study by Arcangeli and Arvin (1995) as cited by

Riser-Roberts (1998), a toluene concentration of > 1 to 3 mg/L reduced the o-xylene

degradation rate and concentration of o-xylene above 2 to 3 mg/L in turn inhibited

toluene biodegradation. With different initial loading of crude oil among the spill

recurrence treatments such inhibitory and/or enhancement effect may have existed.

Significant differences were detected in SOC values among the oiling treatments.

The highest mean SOC values were observed in the four oilings, which had the highest

biodegradation rate of crude oil and heterotrophic microbial activity. Then, why do we

see higher SOC values in the four oilings irrespective of higher microbial activity

assumed to utilize the SOC pool? One possible explanation may be accumulation of the

recalcitrant fraction of the SOC assuming that labile compounds within the SOC fraction

are preferentially utilized by microbes (Marschner and Bredow, 2002). Alternatively, the

supply of SOC supposedly due to higher microbial activity may have exceeded the

demand possibly due availability of a variety of organic substrates.

58

4.5 Conclusions and Implications

The potential for oil spill recurrence in marine ecosystems such as the salt

marshes is significant in view of a variety of spill sources that may be involved. The

present work was carried out to explore the effect of spill recurrence on intrinsic

biodegradation of crude oil in salt marsh intact cores growing Spartina alterniflora.

Specifically, single oil was compared to two, three and four successive oilings, each

totaling to 40-mL (2 L/m2). Residual petroleum hydrocarbons, heterotrophic microbial

activity and soluble organic carbon were monitored for about 3 months.

The results indicate that the biodegradation rate of crude oil, microbial activity

and SOC essentially increased with each subsequent oiling. This suggests that single

oiling exerted relatively toxic effect on the active microbial community as compared to

multiple successive oilings of the same total volume. However, the mechanism by which

the biodegradation rate is accelerated as a result of oil spill recurrence is still uncertain.

Previous studies suggest this to be associated with increased number of oil degrading

microorganisms and/or increased microbial activity, however, the consequence of the

specific oil-degrading enzyme activity is not known.

Both stepwise regression and Pearson correlation indicated no linear relationship

(P>0.05) among the variables measured. This suggests complex interaction among soil

geochemical and microbiological properties in determining the significance of increased

biodegradation of crude oil in salt marshes following oil spill recurrence at least under the

experimental conditions.

The results from this work suggest that, microbial degradation might not be

significant in a pristine tidal marsh particularly immediate to an oil spill event as opposed

to a previously contaminated one. However, a caution need to be made here in that, the

59

results obtained may be limited by the experimental design used in terms of the shorter

time interval between successive oil additions, which was only 5 days.

60

CHAPTER 5

SUMMARY AND OUTLOOK

5.1 Experimental Findings and Implications

The environmental and economic impact of oil contamination in the coastal

marine ecosystems is potentially serious. The microbial degradation of the spilled oil

petroleum hydrocarbons in marine sediments is an important fate process. As a result, it

is a rapidly growing research area with focus to gain fundamental knowledge of the fate

of spilled oil and devising remedial measures that make use of the indigenous microbial

assimilative capacity. Therefore, understanding of the influence of different perturbations

on the fate of spilled oil in marine environment is useful in the assessment of the

environmental impact of oil spills and remedial investigation. The present work

monitored residual petroleum hydrocarbons, heterotrophic microbial activity and soluble

organic carbon (SOC) to determine the effect of flooding and spill recurrence on the

biodegradation of south Louisiana crude oil (SLCO) (2 L/m2) in salt marsh intact cores

incubated for about 3 months.

The results for the flooding study indicate that, the biodegradation of crude oil

fractions (i.e alkanes and PAHs) (with half-lives from 16.50 to 49.51 days and turnover

times from 23.81 to 71.43 days) and microbial activity essentially increased in the order

from the continuously-flooded (CF) regime, intermittently-flooded (IF) regime to non-

flooded (NF) regime. The SOC concentrations increased significantly but opposite to the

trend for crude oil biodegradation and microbial activity. Both stepwise regression and

Pearson correlation revealed no linear relationship among the parameters investigated

61

(P>0.05) implying for complex interaction among the parameters measured at least under

the experimental conditions. Notably, tidal flooding is a natural disturbance occurring in

salt marshes. This reflects that tidal flooding is of vital importance in defining the

ecological characteristics of the salt marshes and indirectly can affect the biodegradation

of spilled oil and decomposition and accumulation of organic matter in salt marshes.

The results for the oil spill recurrence study (single, two, three and four

successive oilings; each treatment totaling to 2 L/m2) indicate that, the biodegradation of

alkanes and PAHs (with half-lives from 11.95 to 69.31 days and turnover times from

17.24 to 100.00 days), heterotrophic microbial activity and SOC concentration essentially

increased with each subsequent oiling. These results suggest that single oiling may have

exerted relatively toxic effect on the active microbial community and associated soil

biogeochemical processes as compared to multiple successive oiling of the same total

volume. Both stepwise regression and Pearson correlation methods revealed no linear

relationship (P > 0.05) among the parameters investigated reflecting for complex

interplay among the parameters at least under the experimental conditions. The results

from the spill recurrence study reflect that, microbial degradation might not be significant

in a pristine tidal marsh particularly immediate to an oil spill event as opposed to a

previously contaminated one. However, a caution need to be made here in that, the results

obtained may be limited by the experimental design used in terms of the shorter time

interval between successive oil additions, which was only 5 days.

The lack of correlation among the parameters measured for both experiments

reflect the challenge in understanding the interrelationship between environmental factors

and microbial ecology in determining the fate of oil spills in salt marshes at least under

62

the experimental conditions. Yet, the inherent microbial assimilative capacity of

petroleum hydrocarbons in salt marshes ought to be fully utilized to provide cost-

effective remedial options. However, the behaviour and fate of spilled oil are site specific

making it difficult to generalize from one case to another.

5.2 Future Research

It is anticipated that, under real field conditions, there may be some variations

from the laboratory results obtained in this work, primarily due to the influence of tidal

mixing and sedimentation and other environmental factors. Therefore, field trials may be

worth undertaking to confirm the laboratory results.

The full extent of the influence of flooding and spill recurrence on microbial

diversity, community structure and their enzymatic activity in oil-contaminated coastal

marshes has yet to be wholly revealed. Therefore, it may be interesting to explore further

on this subject preferably using molecular techniques.

Tidal waters flooding salt marshes are normally turbid with the vegetation

trapping the sediments, resulting in increase in marsh surface elevation with time (Adam,

1990). Sediment burial (accretion) may have a significant role on the fate of crude oil as

it creates additional barrier to oxygen diffusion reaching the contaminated sediments, far

from the overlaying water column. Therefore, it may be of interest to explore the effect of

sediment burial on the biodegradation potential of crude oil.

63

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68

APPENDIX A CALIBRATION CURVES

A1: Microbial Activity (FDA Hydrolysis) Analysis

Figure A1: Calibration curve for FDA hydrolysis analysis using the spectrophotometer A2: SOC Concentration Analysis

Absorbance

0 10000 20000 300

mg

C/ L

0

100

200

300

400

500

Area under the Peak

00 40000

Y = 0.0108x - 0.2025R2 = 1

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

ug F

DA

hyd

roly

zed/

g so

il/hr

0

200

400

600

800

Y = 1231.9x - 253.29R2 = 0.97

Figure A2: Calibration curve for SOC analysis using the TOC Analyzer

69

APPENDIX B RESIDUAL HYDROCARBON DEGRADATION DATA

B1: Effect of Flooding on Phenanthrene and Pyrene Degradation

Time (days)

0 20 40 60 80

Phen

anth

rene

hop

ane

ratio

0

2

4

6

8

10

Non-flooded (NF)Intermittently-flooded (IF)Continously-flooded (CF)

(a) Phenanthrene degradation profile

Time (days)

0 20 40 60 80

Pyre

ne h

opan

e ra

tio0

2

4

6

8

10

Non-flooded (NF)Intermittently-flooded (IF)Continously-flooded (CF)

(b) Pyrene degradation profile

Figure B1: The effect of flooding on degradation of phenanthrene and pyrene

70

B2: Effect of Oil Spill Recurrence on Phenanthrene and Pyrene Degradation

Time (days)

0 20 40 60 80

Phen

anth

rene

hop

ane

ratio

0

2

4

6

8

10

12

Single oilingTwo successive oilingsThree successive oilingsFour successive oilings

(a) Phenanthrene degradation profile

Time (days)

0 20 40 60 80

Pyre

ne h

opan

e ra

tio

0

2

4

6

8

10

12

Single oilingTwo successive oilingThree successive oilingsFour successive oilings

(b) Pyrene degradation profile

Figure B2: The effect of oil spill recurrence on degradation of phenanthrene and pyrene

71

B3: The Effect of Flooding on Biodegradation of Crude oil Fractions

Table B1: Summary data for the degradation of alkanes (n-C10 - n-C36) in the flooding study

CONTINOUSLY-FLOODED (CF)

INTERMITTENTLY-FLOODED(IF)

NON-FLOODED (NF)Time (days)

Total alkane hopane ratio

Standard deviation

Total alkanehopane ratio

Standard deviation

Total alkanehopane ratio

Standard deviation

0 95.64 0.00 95.64 0.00 95.64 0.0020

62.14 38.60 51.07 5.03 34.00 13.6040 40.17 8.82 25.80 19.48 12.40 19.0060 9.52 2.95 7.96 5.07 3.30 1.5380 8.67 2.87 7.30 2.11 4.20 0.00

Table B2: Summary data for the degradation of PAHs in the flooding study

CONTINOUSLY-FLOODED (CF)

INTERMITTENTLY-FLOODED(IF)

NON-FLOODED (NF)Time (days)

Total PAH hopane ratio

Standard deviation

Total PAHhopane ratio

Standard deviation

Total PAHhopane ratio

Standard deviation

0 21.70 0.00 21.70 0.00 21.70 0.0020 15.50

0.22 15.70 0.76 10.80 0.2640 14.20 0.62 12.30 5.33 8.50 0.0060 10.70 7.25 6.99 0.90 3.55 0.3880 5.30 0.52 4.65 0.67 2.65 0.25

72

B4: The Effect of Oil Spill Recurrence on Biodegradation of Crude oil Fractions

Table B3: Summary data for the degradation of alkanes (n-C10 – n-C36) in the oil spill recurrence study

SINGLE OILING TWO OILINGS THREE OILINGS FOUR OILINGS Time (days) Total alkane

hopane ratio Standard deviation

Total alkanehopane ratio

Standard deviation

Total alkanehopane ratio

Standard deviation

Total alkanehopane ratio

Standard deviation

0 78.64 0.00 74.46 0.00 67.53 0.00 65.61 0.0020 38.32

13.11 33.84 11.34 23.61 13.33 20.21 7.1240 11.45 6.20 10.51 2.33 7.62 3.21 5.11 1.5060 9.64 2.20 9.23 3.34 5.83 2.17 4.34 1.3280 9.12 5.60 9.11 2.21 3.49 1.12 2.96 1.11

Table B4: Summary data for the degradation of PAHs in the oil spill recurrence study

SINGLE OILING TWO OILINGS THREE OILINGS FOUR OILINGS Time (days) Total alkane

hopane ratio Standard deviation

Total alkanehopane ratio

Standard deviation

Total alkanehopane ratio

Standard deviation

Total alkanehopane ratio

Standard deviation

0 18.72 0.00 16.13 0.00 12.15 0.00 10.62 0.0020 16.52

7.62 14.25 11.64 8.36 14.13 7.28 14.2540 13.11 9.85 12.58 5.43 6.73 9.72 3.25 10.2260 11.92 8.29 9.83 9.25 3.16 5.76 2.82 5.4680 7.43 5.73 6.82 8.72 2.93 5.26 2.57 5.72

73

APPENDIX C MICROBIAL ACTIVITY (FDA HYDROLYSIS) ANALYSIS DATA

C1: Microbial Activity (FDA Hydrolysis) Data for the Flooding Experiments

Table C1: Summary data of microbial activity (FDA hydrolysis) for the flooding study

CONTROL NON-FLOODED (NF) INTERMITTENTLY_FLOODED

(IF)

CONTINOUSLY-FLOODED

(CF)

Time

(days)

µg FDA

/g-soil/hr

Std

Dev

Moisture

content (%)

µg FDA

/g-soil/hr

Std

Dev

Moisture

content (%)

µg FDA

/g-soil/hr

Std Dev Moisture

content (%)

µg FDA

/g-soil/hr

Std

Dev

Moisture

content

0 178.91 18.95 19.29 183.3 19.17 19.55 163.89 18.19 24.29 215.72 19.79 22.70

20

171.20 18.56 21.29 242.8 12.14 21.51 318.58 12.92 22.14 191.32 18.56 24.10

40 192.03 19.60 23.29 383.2 19.16 18.99 284.30 16.22 19.26 234.91 14.75 19.63

60 183.41 19.17 21.25 302.5 15.13 22.71 302.20 15.11 22.73 259.41 10.97 18.82

80 221.14 11.06 23.70 405.2 20.26 18.92 321.70 13.09 22.21 243.21 19.16 24.56

Notes: The soil samples weighed about 10 g each

74

C2: Microbial Activity (FDA Hydrolysis) Data for the Oil Spill Recurrence Experiments

Table C2: Summary data of microbial activity (FDA hydrolysis) for the oil spill recurrence study

CONTROL SINGLE OILING TWO OILINGS FOUR OILINGS

Time

(days)

µg FDA

/g-soil/hr

Std

Dev

Moisture

content (%)

µg FDA

/g-soil/hr

Std

Dev

Moisture

content (%)

µg FDA

/g-soil/hr

Std Dev Moisture

content (%)

µg FDA

/g-soil/hr

Std

Dev

Moisture

content

0 178.91 18.95 19.29 186.30 18.34 24.29 302.51 18.13 24.22 352.21 13.61 25.18

20

171.20 18.56 21.29 152.40 16.62 21.45 367.90 15.39 18.29 431.50 19.58 22.18

40 192.03 19.60 23.29 163.61 17.18 18.56 356.72 18.84 23.71 482.42 21.12 18.37

60 183.41 19.17 21.25 136.10 18.81 25.30 393.20 19.66 21.14 421.40 17.07 19.29

80 221.14 15.06 23.70 144.32 15.22 18.37 425.33 17.27 19.33 444.31 19.22 21.41

Notes: The soil samples weighed about 10 g each

75

APPENDIX D SOLUBLE ORGANIC CARBON (SOC) DATA

D1: SOC Data for the Flooding Experiments

Table D1: Summary data of SOC analysis for the flooding study

CONTROL

NON-FLOODED (NF)

INTERMITTENTLY_FLOODED

(IF)

CONTINOUSLY-FLOODED

(CF)

Time

(days)

SOC

(ppm)

Std

Dev

Moisture

content (%)

SOC

(ppm)

Std

Dev

Moisture

content (%)

SOC

(ppm)

Std Dev Moisture

content (%)

SOC

(ppm)

Std

Dev

Moisture

content

0 11.2 1.82 19.29 20.34 2.29 19.55 11.94 1.83 24.29 17.84 1.31 22.70

20

12.52 3.22 21.29 15.25 3.22 21.51 15.46 2.93 22.14 22.45 1.82 24.10

40 12.83 2.13 23.29 38.92 2.17 18.99 13.89 0.01 19.26 18.26 2.85 19.63

60 10.33 1.67 21.25 16.68 3.24 22.71 10.37 1.92 22.73 32.45 1.99 18.82

80 7.83 3.81 23.70 17.56 2.11 18.92 15.46 2.85 22.21 18.26 2.14 24.56

Notes: The soil samples weighed about 10 g

76

D2: SOC Data for the Spill Recurrence Experiments

Table D2: Summary data of SOC analysis for the oil spill recurrence study

CONTROL SINGLE OILING TWO OILINGS FOUR OILINGS

Time

(days)

SOC

(ppm)

Std

Dev

Moisture

content (%)

SOC

(ppm)

Std

Dev

Moisture

content (%)

SOC

(ppm)

Std Dev Moisture

content (%)

SOC

(ppm)

Std

Dev

Moisture

content (%)

0 11.2 1.82 19.29 22.46 2.64 24.29 28.97 1.28 24.22 17.63 0.13 25.18

20

12.52 3.22 21.29 15.07 1.22 21.45 24.97 6.93 18.29 22.49 7.95 22.18

40 12.83 2.13 23.29 16.06 3.56 18.56 18.38 5.35 23.71 21.33 2.67 18.37

60 10.33 1.67 21.25 17.22 2.63 25.30 21.42 4.40 21.14 20.42 2.98 19.29

80 7.83 3.81 23.70 15.21 1.53 18.37 18.52 4.25 19.33 18.69 1.93 21.41

Notes: The soil samples weighed about 10 g

77

APPENDIX E SAMPLE STEPWISE REGRESSION OUTPUT

E1: Brief Description

The stepwise linear regression is used when

•

•

Predicting a trend in the data, or predict the value of one variable from the

values of one or more other variables, by fitting a line or plane (or hyperplane)

through the data

Finding the model with suitable independent variables by adding or removing

independent variables from the equation

E2: Sample Output from Stepwise Regression

The data used for this sample output were those for alkanes under the CF regime.

The output from the stepwise regression follows below.

Forward Stepwise Regression:

Dependent Variable: Col 1(Total alkane hopane ratio)

F-to-Enter: 4.0000 P = 0.1161

F-to-Remove: 3.9000 P = 0.1195 Step 0:

Standard Error of Estimate = 36.9

Analysis of Variance: Group DF SS MS Residual 4 5444.5 1361.1

Group F P Residual Variables in Model

Group Coef. Std. Coeff. Std. Error Constant 43.23 16.499

78

Group F-to-Remove P Constant

Variables not in Model Group F-to-Enter P Col 2 (FDA hydrolyzed) 4.089 0.1132 Col 3(SOC concentration) 0.801 0.4214

Step 1: Col 2 (FDA hydrolozed) Entered

R = 0.7595 Rsqr = 0.5768 Adj Rsqr = 0.4357 Standard Error of Estimate = 27.7 Analysis of Variance:

Group DF SS MS Regression 1 3140.4 3140.4 Residual 3 2304.1 768.0

Group F P Regression 4.09 0.1364 Residual

Variables in Model

Group Coef. Std. Coeff. Std. Error Constant 287.51 121.441 Col 2 (FDA hydrolyzed) -1.07 - 0.759 0.528

Group F-to-Remove P Constant Col 2 (FDA hydrolyzed) 4.09 0.1364 Variables not in Model

Group F-to-Enter P Col 3 (SOC concentration) 0.107 0.7650

Summary Table Step # Vars. Entered Vars. Removed R 1 Col 2 (FDA hydrolyzed) 0.759 Step # RSqr Delta RSqr Vars in Model 1 0.577 0.577 1 The dependent variable Col 1 (alkane hopane ratio) can be predicted from a linear combination of the independent variables: P Col 2 (FDA hydrolyzed) 0.1364 The following variables did not significantly add to the ability of the equation to predict Col 1 (Alkane hopane ratio) and were not included in the final equation: Col 3 (SOC concentration) Normality Test: Passed (P = 0.3762) Homoscedasticity Test: Passed (P = 0.0500) Power of performed test with alpha = 0.0500: 0.2902 The power of the performed test (0.2902) is below the desired power of 0.8000. You should interpret the negative findings cautiously.

79

APPENDIX F PEARSON CORRELATION RESULTS

F1: Brief Description

Pearson correlation method is used to measure the strength of association between

pairs of variables without regard to which variable is dependent or independent; and tests

whether relationship, if any, between the variables is a straight line.

The P-value refers to the probability of being wrong in concluding that there is a

true association between the variables. In our case, if P>0.05, this reflects that there are

no significant relationships between the pair of variables in the correlation table.

F2: Pearson Correlation for Intermittently-flooded (IF) Regime

Table F1: The P-values for Pearson correlation for the alkanes under the IF regime

Total alkane hopane

ratio

FDA Hydrolyzed SOC concentration

Total alkane hopane ratio - 0.0727 0.8640

FDA Hydrolyzed 0.7270 - 0.436

SOC concentration 0.8640 0.4360 -

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Table F2: The P-values for Pearson correlation of PAHs under the IF regime

Total PAH hopane

ratio

FDA Hydrolyzed SOC concentration

Total PAH hopane ratio - 0.120 0.867

FDA Hydrolyzed 0.120 - 0.436

SOC concentration 0.867 0.436 -

F3: Pearson Correlation for the Four Oiling Treatment

Table F3: The P-values for Pearson correlation of alkanes in the four oiling treatment

Total alkane hopane

ratio

FDA Hydrolyzed SOC concentration

Total alkane hopane ratio - 0.0598 0.3550

FDA Hydrolyzed 0.0598 - 0.2480

SOC concentration 0.3550 0.2480 -

Table F4: The P-values for Pearson correlation of PAHs in the four oiling treatment

Total PAH hopane

ratio

FDA Hydrolyzed SOC concentration

Total PAH hopane ratio - 0.1060 0.6440

FDA Hydrolyzed 0.1060 - 0.2480

SOC concentration 0.6440 0.2480 -

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VITA

Julius Enock was born on July 14, 1973, in Dar-es-salaam, Tanzania. He

graduated from the University of Dar-es-salaam, Tanzania, in November, 1998, with a

Bachelor of Science degree in chemical and process engineering.

Following graduation, he worked temporarily with the Institute of Production

Innovation (IPI), University of Dar-es-salaam, Tanzania, as a research assistant for about

a year. Then, in March, 2000, he joined the Division of Environment in the Vice

President’s Office (Tanzania), as an Environmental Engineer.

In August, 2000 he was awarded ATLAS (African Training for Leadership and

Skills) scholarship to pursue a master's degree in civil engineering, majoring in

environmental engineering, at Louisiana State University. He shall graduate in August,

2002.

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